diff --git a/.github/workflows/docker-bases.yml b/.github/workflows/docker-bases.yml
index 9d964c6548..090ff631da 100644
--- a/.github/workflows/docker-bases.yml
+++ b/.github/workflows/docker-bases.yml
@@ -255,4 +255,6 @@ jobs:
           file: './docker/Dockerfile.amd'
           push: true
           target: 'hip'
+          build-args: |
+            ROCM_VERSION=6.3.4
           tags: devitocodes/bases:amd-hip
diff --git a/devito/__init__.py b/devito/__init__.py
index b8d7621297..e6cf0092bf 100644
--- a/devito/__init__.py
+++ b/devito/__init__.py
@@ -56,7 +56,8 @@ def reinit_compiler(val):
     """
     Re-initialize the Compiler.
     """
-    configuration['compiler'].__init__(suffix=configuration['compiler'].suffix,
+    configuration['compiler'].__init__(name=configuration['compiler'].name,
+                                       suffix=configuration['compiler'].suffix,
                                        mpi=configuration['mpi'])
     return val
 
@@ -65,7 +66,7 @@ def reinit_compiler(val):
 configuration.add('platform', 'cpu64', list(platform_registry),
                   callback=lambda i: platform_registry[i]())
 configuration.add('compiler', 'custom', compiler_registry,
-                  callback=lambda i: compiler_registry[i]())
+                  callback=lambda i: compiler_registry[i](name=i))
 
 # Setup language for shared-memory parallelism
 preprocessor = lambda i: {0: 'C', 1: 'openmp'}.get(i, i)  # Handles DEVITO_OPENMP deprec
diff --git a/devito/arch/archinfo.py b/devito/arch/archinfo.py
index 6987ce3cb6..04bd30bcbf 100644
--- a/devito/arch/archinfo.py
+++ b/devito/arch/archinfo.py
@@ -330,6 +330,69 @@ def cbk(deviceid=0):
     except OSError:
         pass
 
+    # *** Third try: `sycl-ls`, clearly only works with Intel cards
+    try:
+        gpu_infos = {}
+
+        # sycl-ls sometimes finds gpu twice with opencl and without so
+        # we need to make sure we don't get duplicates
+        selected_platform = None
+        platform_block = ""
+
+        proc = Popen(["sycl-ls", "--verbose"], stdout=PIPE, stderr=DEVNULL, text=True)
+        sycl_output, _ = proc.communicate()
+
+        # Extract platform blocks
+        platforms = re.findall(r"Platform \[#(\d+)\]:([\s\S]*?)(?=Platform \[#\d+\]:|$)",
+                               sycl_output)
+
+        # Select Level-Zero if available, otherwise use OpenCL
+        for platform_id, platform_content in platforms:
+            if "Intel(R) Level-Zero" in platform_content:
+                selected_platform = platform_id
+                platform_block = platform_content
+                break
+            elif "Intel(R) OpenCL Graphics" in platform_content and \
+                    selected_platform is None:
+                selected_platform = platform_id
+                platform_block = platform_content
+
+        # Extract GPU devices from the selected platform
+        devices = re.findall(r"Device \[#(\d+)\]:([\s\S]*?)(?=Device \[#\d+\]:|$)",
+                             platform_block)
+
+        for device_id, device_block in devices:
+            if re.search(r"^\s*Type\s*:\s*gpu", device_block, re.MULTILINE):
+                name_match = re.search(r"^\s*Name\s*:\s*(.+)", device_block, re.MULTILINE)
+
+                if name_match:
+                    name = name_match.group(1).strip()
+
+                    # Store GPU info with correct physical ID
+                    gpu_infos[device_id] = {
+                        "physicalid": device_id,
+                        "product": name
+                    }
+
+        gpu_info = homogenise_gpus(list(gpu_infos.values()))
+
+        # Also attach callbacks to retrieve instantaneous memory info
+        # Now this should be done using xpu-smi but for some reason
+        # it throws a lot of weird errors in docker so skipping for now
+        for i in ['total', 'free', 'used']:
+            def make_cbk(i):
+                def cbk(deviceid=0):
+                    return None
+                return cbk
+
+            gpu_info['mem.%s' % i] = make_cbk(i)
+
+        gpu_infos['architecture'] = 'Intel'
+        return gpu_info
+
+    except OSError:
+        pass
+
     # *** Second try: `lshw`
     try:
         info_cmd = ['lshw', '-C', 'video']
@@ -391,7 +454,7 @@ def parse_product_arch():
         gpu_infos = []
         for line in lines:
             # Graphics cards are listed as VGA or 3D controllers in lspci
-            if 'VGA' in line or '3D' in line:
+            if any(i in line for i in ('VGA', '3D', 'Display')):
                 gpu_info = {}
                 # Lines produced by lspci command are of the form:
                 #   xxxx:xx:xx.x Device Type: Name
diff --git a/devito/arch/compiler.py b/devito/arch/compiler.py
index aa8cfa0795..a2b9334f54 100644
--- a/devito/arch/compiler.py
+++ b/devito/arch/compiler.py
@@ -180,12 +180,21 @@ def __init__(self):
     """
 
     fields = {'cc', 'ld'}
-    _cpp = False
+    _default_cpp = False
+    _cxxstd = 'c++14'
+    _cstd = 'c99'
 
     def __init__(self, **kwargs):
+        maybe_name = kwargs.pop('name', self.__class__.__name__)
+        if isinstance(maybe_name, Compiler):
+            self._name = maybe_name.name
+        else:
+            self._name = maybe_name
+
         super().__init__(**kwargs)
 
         self.__lookup_cmds__()
+        self._cpp = kwargs.get('cpp', self._default_cpp)
 
         self.suffix = kwargs.get('suffix')
         if not kwargs.get('mpi'):
@@ -195,7 +204,7 @@ def __init__(self, **kwargs):
             self.cc = self.MPICC if self._cpp is False else self.MPICXX
         self.ld = self.cc  # Wanted by the superclass
 
-        self.cflags = ['-O3', '-g', '-fPIC', '-Wall', '-std=c99']
+        self.cflags = ['-O3', '-g', '-fPIC', '-Wall', f'-std={self.std}']
         self.ldflags = ['-shared']
 
         self.include_dirs = []
@@ -225,13 +234,13 @@ def __new_with__(self, **kwargs):
         Create a new Compiler from an existing one, inherenting from it
         the flags that are not specified via ``kwargs``.
         """
-        return self.__class__(suffix=kwargs.pop('suffix', self.suffix),
+        return self.__class__(name=self.name, suffix=kwargs.pop('suffix', self.suffix),
                               mpi=kwargs.pop('mpi', configuration['mpi']),
                               **kwargs)
 
     @property
     def name(self):
-        return self.__class__.__name__
+        return self._name
 
     @property
     def version(self):
@@ -247,6 +256,10 @@ def version(self):
 
         return version
 
+    @property
+    def std(self):
+        return self._cxxstd if self._cpp else self._cstd
+
     def get_version(self):
         result, stdout, stderr = call_capture_output((self.cc, "--version"))
         if result != 0:
@@ -482,7 +495,7 @@ def __init_finalize__(self, **kwargs):
         platform = kwargs.pop('platform', configuration['platform'])
 
         if isinstance(platform, NvidiaDevice):
-            self.cflags.remove('-std=c99')
+            self.cflags.remove(f'-std={self.std}')
             # Add flags for OpenMP offloading
             if language in ['C', 'openmp']:
                 cc = get_nvidia_cc()
@@ -490,7 +503,7 @@ def __init_finalize__(self, **kwargs):
                     self.cflags += ['-Xopenmp-target', f'-march=sm_{cc}']
                 self.ldflags += ['-fopenmp', '-fopenmp-targets=nvptx64-nvidia-cuda']
         elif platform is AMDGPUX:
-            self.cflags.remove('-std=c99')
+            self.cflags.remove(f'-std={self.std}')
             # Add flags for OpenMP offloading
             if language in ['C', 'openmp']:
                 self.ldflags += ['-target', 'x86_64-pc-linux-gnu']
@@ -553,9 +566,9 @@ def __init_finalize__(self, **kwargs):
             self.ldflags += ['-fopenmp']
 
         if isinstance(platform, NvidiaDevice):
-            self.cflags.remove('-std=c99')
+            self.cflags.remove(f'-std={self.std}')
         elif platform is AMDGPUX:
-            self.cflags.remove('-std=c99')
+            self.cflags.remove(f'-std={self.std}')
             # Add flags for OpenMP offloading
             if language in ['C', 'openmp']:
                 self.ldflags += ['-target', 'x86_64-pc-linux-gnu']
@@ -590,16 +603,13 @@ def __lookup_cmds__(self):
 
 class PGICompiler(Compiler):
 
-    _cpp = True
+    _default_cpp = True
 
     def __init_finalize__(self, **kwargs):
 
-        self.cflags.remove('-std=c99')
         self.cflags.remove('-O3')
         self.cflags.remove('-Wall')
 
-        self.cflags.append('-std=c++11')
-
         language = kwargs.pop('language', configuration['language'])
         platform = kwargs.pop('platform', configuration['platform'])
 
@@ -643,14 +653,13 @@ def __lookup_cmds__(self):
 
 class CudaCompiler(Compiler):
 
-    _cpp = True
+    _default_cpp = True
 
     def __init_finalize__(self, **kwargs):
 
-        self.cflags.remove('-std=c99')
         self.cflags.remove('-Wall')
         self.cflags.remove('-fPIC')
-        self.cflags.extend(['-std=c++14', '-Xcompiler', '-fPIC'])
+        self.cflags.extend(['-Xcompiler', '-fPIC'])
 
         if configuration['mpi']:
             # We rather use `nvcc` to compile MPI, but for this we have to
@@ -717,15 +726,10 @@ def __lookup_cmds__(self):
 
 class HipCompiler(Compiler):
 
-    _cpp = True
+    _default_cpp = True
 
     def __init_finalize__(self, **kwargs):
 
-        self.cflags.remove('-std=c99')
-        self.cflags.remove('-Wall')
-        self.cflags.remove('-fPIC')
-        self.cflags.extend(['-std=c++14', '-fPIC'])
-
         if configuration['mpi']:
             # We rather use `hipcc` to compile MPI, but for this we have to
             # explicitly pass the flags that an `mpicc` would implicitly use
@@ -831,7 +835,7 @@ def __init_finalize__(self, **kwargs):
         language = kwargs.pop('language', configuration['language'])
 
         if language == 'sycl':
-            raise ValueError("Use SyclCompiler to jit-compile sycl")
+            warning(f"Use SyclCompiler (`sycl`) to jit-compile sycl, not {self.name}")
 
         elif language == 'openmp':
             # Earlier versions to OneAPI 2023.2.0 (clang17 underneath), have an
@@ -878,7 +882,7 @@ def __lookup_cmds__(self):
 
 class SyclCompiler(OneapiCompiler):
 
-    _cpp = True
+    _default_cpp = True
 
     def __init_finalize__(self, **kwargs):
         IntelCompiler.__init_finalize__(self, **kwargs)
@@ -887,9 +891,9 @@ def __init_finalize__(self, **kwargs):
         language = kwargs.pop('language', configuration['language'])
 
         if language != 'sycl':
-            raise ValueError("Expected language sycl with SyclCompiler")
+            warning(f"Expected language sycl with SyclCompiler, not {language}")
 
-        self.cflags.remove('-std=c99')
+        self.cflags.remove(f'-std={self.std}')
         self.cflags.append('-fsycl')
 
         self.cflags.remove('-g')  # -g disables some optimizations in IGC
@@ -903,7 +907,7 @@ def __init_finalize__(self, **kwargs):
         elif isinstance(platform, IntelDevice):
             self.cflags.append('-fsycl-targets=spir64')
         else:
-            raise NotImplementedError(f"Unsupported platform {platform}")
+            warning(f"Unsupported platform {platform}")
 
 
 class CustomCompiler(Compiler):
@@ -945,7 +949,6 @@ def __new__(cls, *args, **kwargs):
         obj = super().__new__(cls)
         # Keep base to initialize accordingly
         obj._base = kwargs.pop('base', _base)
-        obj._cpp = obj._base._cpp
 
         return obj
 
@@ -976,6 +979,10 @@ def __lookup_cmds__(self):
     def __new_with__(self, **kwargs):
         return super().__new_with__(base=self._base, **kwargs)
 
+    @property
+    def _default_cpp(self):
+        return self._base._default_cpp
+
 
 class CompilerRegistry(dict):
     """
@@ -984,15 +991,19 @@ class CompilerRegistry(dict):
     """
 
     def __getitem__(self, key):
+        if isinstance(key, Compiler):
+            key = key.name
+
         if key.startswith('gcc-'):
             i = key.split('-')[1]
             return partial(GNUCompiler, suffix=i)
+
         return super().__getitem__(key)
 
-    def __contains__(self, k):
-        if isinstance(k, Compiler):
-            k = k.name
-        return k in self.keys() or k.startswith('gcc-')
+    def __contains__(self, key):
+        if isinstance(key, Compiler):
+            key = key.name
+        return key in self.keys() or key.startswith('gcc-')
 
 
 _compiler_registry = {
@@ -1011,6 +1022,7 @@ def __contains__(self, k):
     'nvc++': NvidiaCompiler,
     'nvidia': NvidiaCompiler,
     'cuda': CudaCompiler,
+    'nvcc': CudaCompiler,
     'osx': ClangCompiler,
     'intel': OneapiCompiler,
     'icx': OneapiCompiler,
diff --git a/devito/builtins/arithmetic.py b/devito/builtins/arithmetic.py
index 93fbc917b5..5e807219f5 100644
--- a/devito/builtins/arithmetic.py
+++ b/devito/builtins/arithmetic.py
@@ -32,13 +32,13 @@ def norm(f, order=2):
     s = dv.types.Symbol(name='sum', dtype=n.dtype)
 
     op = dv.Operator([dv.Eq(s, 0.0)] + eqns +
-                     [dv.Inc(s, dv.Abs(Pow(p, order))), dv.Eq(n[0], s)],
+                     [dv.Inc(s, Pow(dv.Abs(p), order)), dv.Eq(n[0], s)],
                      name='norm%d' % order)
     op.apply(**kwargs)
 
     v = np.power(n.data[0], 1/order)
 
-    return f.dtype(v)
+    return np.real(f.dtype(v))
 
 
 @dv.switchconfig(log_level='ERROR')
diff --git a/devito/builtins/utils.py b/devito/builtins/utils.py
index 67aef28ba0..a83dd765a8 100644
--- a/devito/builtins/utils.py
+++ b/devito/builtins/utils.py
@@ -23,7 +23,10 @@
     # NOTE: np.float128 isn't really a thing, see for example
     # https://github.com/numpy/numpy/issues/10288
     # https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html#1070
-    np.float64: np.float64
+    np.float64: np.float64,
+    # ComplexX accumulates on Complex2X
+    np.complex64: np.complex128,
+    np.complex128: np.complex128,
 }
 
 
diff --git a/devito/core/__init__.py b/devito/core/__init__.py
index 507c5edf15..5b7ad7878b 100644
--- a/devito/core/__init__.py
+++ b/devito/core/__init__.py
@@ -1,41 +1,75 @@
 from devito.arch import Cpu64, Intel64, Arm, Power, Device
-from devito.core.cpu import (Cpu64NoopCOperator, Cpu64NoopOmpOperator,
-                             Cpu64AdvCOperator, Cpu64AdvOmpOperator,
-                             Cpu64FsgCOperator, Cpu64FsgOmpOperator,
-                             Cpu64CustomOperator)
-from devito.core.intel import (Intel64AdvCOperator, Intel64AdvOmpOperator,
-                               Intel64FsgCOperator, Intel64FsgOmpOperator)
-from devito.core.arm import ArmAdvCOperator, ArmAdvOmpOperator
-from devito.core.power import PowerAdvCOperator, PowerAdvOmpOperator
-from devito.core.gpu import (DeviceNoopOmpOperator, DeviceNoopAccOperator,
-                             DeviceAdvOmpOperator, DeviceAdvAccOperator,
-                             DeviceFsgOmpOperator, DeviceFsgAccOperator,
-                             DeviceCustomOmpOperator, DeviceCustomAccOperator)
+from devito.core.cpu import (
+    Cpu64NoopCOperator, Cpu64NoopOmpOperator,
+    Cpu64AdvCOperator, Cpu64AdvOmpOperator,
+    Cpu64FsgCOperator, Cpu64FsgOmpOperator,
+    Cpu64CustomOperator, Cpu64CustomCXXOperator,
+    Cpu64CXXNoopCOperator, Cpu64CXXNoopOmpOperator,
+    Cpu64AdvCXXOperator, Cpu64AdvCXXOmpOperator,
+    Cpu64FsgCXXOperator, Cpu64FsgCXXOmpOperator
+)
+from devito.core.intel import (
+    Intel64AdvCOperator, Intel64AdvOmpOperator,
+    Intel64FsgCOperator, Intel64FsgOmpOperator,
+    Intel64CXXAdvCOperator, Intel64AdvCXXOmpOperator,
+    Intel64FsgCXXOperator, Intel64FsgCXXOmpOperator
+)
+from devito.core.arm import (
+    ArmAdvCOperator, ArmAdvOmpOperator,
+    ArmAdvCXXOperator, ArmAdvCXXOmpOperator
+)
+from devito.core.power import (
+    PowerAdvCOperator, PowerAdvOmpOperator,
+    PowerCXXAdvCOperator, PowerAdvCXXOmpOperator
+)
+from devito.core.gpu import (
+    DeviceNoopOmpOperator, DeviceNoopAccOperator,
+    DeviceAdvOmpOperator, DeviceAdvAccOperator,
+    DeviceFsgOmpOperator, DeviceFsgAccOperator,
+    DeviceCustomOmpOperator, DeviceCustomAccOperator
+)
 from devito.operator.registry import operator_registry
 
 # Register CPU Operators
 operator_registry.add(Cpu64CustomOperator, Cpu64, 'custom', 'C')
 operator_registry.add(Cpu64CustomOperator, Cpu64, 'custom', 'openmp')
+operator_registry.add(Cpu64CustomCXXOperator, Cpu64, 'custom', 'CXX')
+operator_registry.add(Cpu64CustomCXXOperator, Cpu64, 'custom', 'CXXopenmp')
 
 operator_registry.add(Cpu64NoopCOperator, Cpu64, 'noop', 'C')
 operator_registry.add(Cpu64NoopOmpOperator, Cpu64, 'noop', 'openmp')
+operator_registry.add(Cpu64CXXNoopCOperator, Cpu64, 'noop', 'CXX')
+operator_registry.add(Cpu64CXXNoopOmpOperator, Cpu64, 'noop', 'CXXopenmp')
 
 operator_registry.add(Cpu64AdvCOperator, Cpu64, 'advanced', 'C')
 operator_registry.add(Cpu64AdvOmpOperator, Cpu64, 'advanced', 'openmp')
+operator_registry.add(Cpu64AdvCXXOperator, Cpu64, 'advanced', 'CXX')
+operator_registry.add(Cpu64AdvCXXOmpOperator, Cpu64, 'advanced', 'CXXopenmp')
 
 operator_registry.add(Cpu64FsgCOperator, Cpu64, 'advanced-fsg', 'C')
 operator_registry.add(Cpu64FsgOmpOperator, Cpu64, 'advanced-fsg', 'openmp')
+operator_registry.add(Cpu64FsgCXXOperator, Cpu64, 'advanced-fsg', 'CXX')
+operator_registry.add(Cpu64FsgCXXOmpOperator, Cpu64, 'advanced-fsg', 'CXXopenmp')
 
 operator_registry.add(Intel64AdvCOperator, Intel64, 'advanced', 'C')
 operator_registry.add(Intel64AdvOmpOperator, Intel64, 'advanced', 'openmp')
+operator_registry.add(Intel64CXXAdvCOperator, Intel64, 'advanced', 'CXX')
+operator_registry.add(Intel64AdvCXXOmpOperator, Intel64, 'advanced', 'CXXopenmp')
+
 operator_registry.add(Intel64FsgCOperator, Intel64, 'advanced-fsg', 'C')
 operator_registry.add(Intel64FsgOmpOperator, Intel64, 'advanced-fsg', 'openmp')
+operator_registry.add(Intel64FsgCXXOperator, Intel64, 'advanced-fsg', 'CXX')
+operator_registry.add(Intel64FsgCXXOmpOperator, Intel64, 'advanced-fsg', 'CXXopenmp')
 
 operator_registry.add(ArmAdvCOperator, Arm, 'advanced', 'C')
 operator_registry.add(ArmAdvOmpOperator, Arm, 'advanced', 'openmp')
+operator_registry.add(ArmAdvCXXOperator, Arm, 'advanced', 'CXX')
+operator_registry.add(ArmAdvCXXOmpOperator, Arm, 'advanced', 'CXXopenmp')
 
 operator_registry.add(PowerAdvCOperator, Power, 'advanced', 'C')
 operator_registry.add(PowerAdvOmpOperator, Power, 'advanced', 'openmp')
+operator_registry.add(PowerCXXAdvCOperator, Power, 'advanced', 'CXX')
+operator_registry.add(PowerAdvCXXOmpOperator, Power, 'advanced', 'CXXopenmp')
 
 # Register Device Operators
 operator_registry.add(DeviceCustomOmpOperator, Device, 'custom', 'C')
diff --git a/devito/core/arm.py b/devito/core/arm.py
index 0b765c1b52..f990ef31e0 100644
--- a/devito/core/arm.py
+++ b/devito/core/arm.py
@@ -1,19 +1,23 @@
-from devito.core.cpu import Cpu64AdvOperator
-from devito.passes.iet import CTarget, OmpTarget
+from devito.core.cpu import (Cpu64AdvOperator, Cpu64AdvCXXOperator,
+                             Cpu64AdvCOperator)
+from devito.passes.iet import OmpTarget, CXXOmpTarget
 
-__all__ = ['ArmAdvCOperator', 'ArmAdvOmpOperator']
+__all__ = ['ArmAdvCOperator', 'ArmAdvOmpOperator', 'ArmAdvCXXOperator',
+           'ArmAdvCXXOmpOperator']
 
 
-class ArmAdvOperator(Cpu64AdvOperator):
-    pass
+ArmAdvOperator = Cpu64AdvOperator
+ArmAdvCOperator = Cpu64AdvCOperator
+ArmAdvCXXOperator = Cpu64AdvCXXOperator
 
 
-class ArmAdvCOperator(ArmAdvOperator):
-    _Target = CTarget
-
-
-class ArmAdvOmpOperator(ArmAdvOperator):
+class ArmAdvOmpOperator(ArmAdvCOperator):
     _Target = OmpTarget
 
     # Avoid nested parallelism on ThunderX2
     PAR_NESTED = 4
+
+
+class ArmAdvCXXOmpOperator(ArmAdvOmpOperator):
+    _Target = CXXOmpTarget
+    LINEARIZE = True
diff --git a/devito/core/cpu.py b/devito/core/cpu.py
index b9baedb237..d96d03e31d 100644
--- a/devito/core/cpu.py
+++ b/devito/core/cpu.py
@@ -8,14 +8,16 @@
 from devito.passes.clusters import (Lift, blocking, buffering, cire, cse,
                                     factorize, fission, fuse, optimize_pows,
                                     optimize_hyperplanes)
-from devito.passes.iet import (CTarget, OmpTarget, avoid_denormals, linearize,
+from devito.passes.iet import (CTarget, CXXTarget, COmpTarget, CXXOmpTarget,
+                               avoid_denormals, linearize,
                                mpiize, hoist_prodders, relax_incr_dimensions,
                                check_stability)
 from devito.tools import timed_pass
 
 __all__ = ['Cpu64NoopCOperator', 'Cpu64NoopOmpOperator', 'Cpu64AdvCOperator',
            'Cpu64AdvOmpOperator', 'Cpu64FsgCOperator', 'Cpu64FsgOmpOperator',
-           'Cpu64CustomOperator']
+           'Cpu64CustomOperator', 'Cpu64CustomCXXOperator', 'Cpu64AdvCXXOperator',
+           'Cpu64AdvCXXOmpOperator', 'Cpu64FsgCXXOperator', 'Cpu64FsgCXXOmpOperator']
 
 
 class Cpu64OperatorMixin:
@@ -77,11 +79,12 @@ def _normalize_kwargs(cls, **kwargs):
 
         # Misc
         o['opt-comms'] = oo.pop('opt-comms', True)
-        o['linearize'] = oo.pop('linearize', False)
+        o['linearize'] = oo.pop('linearize', cls.LINEARIZE)
         o['mapify-reduce'] = oo.pop('mapify-reduce', cls.MAPIFY_REDUCE)
         o['index-mode'] = oo.pop('index-mode', cls.INDEX_MODE)
         o['place-transfers'] = oo.pop('place-transfers', True)
         o['errctl'] = oo.pop('errctl', cls.ERRCTL)
+        o['scalar-min-type'] = oo.pop('scalar-min-type', cls.SCALAR_MIN_TYPE)
 
         # Recognised but unused by the CPU backend
         oo.pop('par-disabled', None)
@@ -244,7 +247,7 @@ def _normalize_kwargs(cls, **kwargs):
 
 class Cpu64CustomOperator(Cpu64OperatorMixin, CustomOperator):
 
-    _Target = OmpTarget
+    _Target = COmpTarget
 
     @classmethod
     def _make_dsl_passes_mapper(cls, **kwargs):
@@ -317,6 +320,11 @@ def _make_iet_passes_mapper(cls, **kwargs):
     assert not (set(_known_passes) & set(_known_passes_disabled))
 
 
+class Cpu64CustomCXXOperator(Cpu64CustomOperator):
+
+    _Target = CXXTarget
+    LINEARIZE = True
+
 # Language level
 
 
@@ -324,21 +332,51 @@ class Cpu64NoopCOperator(Cpu64NoopOperator):
     _Target = CTarget
 
 
+class Cpu64CXXNoopCOperator(Cpu64NoopOperator):
+    _Target = CXXTarget
+    LINEARIZE = True
+
+
 class Cpu64NoopOmpOperator(Cpu64NoopOperator):
-    _Target = OmpTarget
+    _Target = COmpTarget
+
+
+class Cpu64CXXNoopOmpOperator(Cpu64NoopOperator):
+    _Target = CXXOmpTarget
+    LINEARIZE = True
 
 
 class Cpu64AdvCOperator(Cpu64AdvOperator):
     _Target = CTarget
 
 
+class Cpu64AdvCXXOperator(Cpu64AdvOperator):
+    _Target = CXXTarget
+    LINEARIZE = True
+
+
 class Cpu64AdvOmpOperator(Cpu64AdvOperator):
-    _Target = OmpTarget
+    _Target = COmpTarget
+
+
+class Cpu64AdvCXXOmpOperator(Cpu64AdvOperator):
+    _Target = CXXOmpTarget
+    LINEARIZE = True
 
 
 class Cpu64FsgCOperator(Cpu64FsgOperator):
     _Target = CTarget
 
 
+class Cpu64FsgCXXOperator(Cpu64FsgOperator):
+    _Target = CXXTarget
+    LINEARIZE = True
+
+
 class Cpu64FsgOmpOperator(Cpu64FsgOperator):
-    _Target = OmpTarget
+    _Target = COmpTarget
+
+
+class Cpu64FsgCXXOmpOperator(Cpu64FsgOperator):
+    _Target = CXXOmpTarget
+    LINEARIZE = True
diff --git a/devito/core/gpu.py b/devito/core/gpu.py
index 0e42b4886c..37a2e7228d 100644
--- a/devito/core/gpu.py
+++ b/devito/core/gpu.py
@@ -95,6 +95,7 @@ def _normalize_kwargs(cls, **kwargs):
         o['index-mode'] = oo.pop('index-mode', cls.INDEX_MODE)
         o['place-transfers'] = oo.pop('place-transfers', True)
         o['errctl'] = oo.pop('errctl', cls.ERRCTL)
+        o['scalar-min-type'] = oo.pop('scalar-min-type', cls.SCALAR_MIN_TYPE)
 
         if oo:
             raise InvalidOperator("Unsupported optimization options: [%s]"
diff --git a/devito/core/intel.py b/devito/core/intel.py
index 3b8f8b0208..9e378ffc12 100644
--- a/devito/core/intel.py
+++ b/devito/core/intel.py
@@ -1,11 +1,18 @@
 from devito.core.cpu import (Cpu64AdvCOperator, Cpu64AdvOmpOperator,
-                             Cpu64FsgCOperator, Cpu64FsgOmpOperator)
+                             Cpu64FsgCOperator, Cpu64FsgOmpOperator,
+                             Cpu64AdvCXXOperator, Cpu64AdvCXXOmpOperator,
+                             Cpu64FsgCXXOperator, Cpu64FsgCXXOmpOperator)
 
 __all__ = ['Intel64AdvCOperator', 'Intel64AdvOmpOperator', 'Intel64FsgCOperator',
-           'Intel64FsgOmpOperator']
+           'Intel64FsgOmpOperator', 'Intel64CXXAdvCOperator', 'Intel64AdvCXXOmpOperator',
+           'Intel64FsgCXXOperator', 'Intel64FsgCXXOmpOperator']
 
 
 Intel64AdvCOperator = Cpu64AdvCOperator
 Intel64AdvOmpOperator = Cpu64AdvOmpOperator
 Intel64FsgCOperator = Cpu64FsgCOperator
 Intel64FsgOmpOperator = Cpu64FsgOmpOperator
+Intel64CXXAdvCOperator = Cpu64AdvCXXOperator
+Intel64AdvCXXOmpOperator = Cpu64AdvCXXOmpOperator
+Intel64FsgCXXOperator = Cpu64FsgCXXOperator
+Intel64FsgCXXOmpOperator = Cpu64FsgCXXOmpOperator
diff --git a/devito/core/operator.py b/devito/core/operator.py
index e6bfd18916..cd094ed04b 100644
--- a/devito/core/operator.py
+++ b/devito/core/operator.py
@@ -1,6 +1,8 @@
 from collections.abc import Iterable
 from functools import cached_property
 
+import numpy as np
+
 from devito.core.autotuning import autotune
 from devito.exceptions import InvalidOperator
 from devito.ir import FindSymbols
@@ -67,6 +69,11 @@ class BasicOperator(Operator):
     intensity of the generated kernel.
     """
 
+    SCALAR_MIN_TYPE = np.float16
+    """
+    Minimum datatype for a scalar arising from a common sub-expression or CIRE temp.
+    """
+
     PAR_COLLAPSE_NCORES = 4
     """
     Use a collapse clause if the number of available physical cores is greater
@@ -131,6 +138,11 @@ class BasicOperator(Operator):
     (default) or `int64`.
     """
 
+    LINEARIZE = False
+    """
+    Linearize n-dimensional Indexeds.
+    """
+
     ERRCTL = None
     """
     Runtime error checking. If this option is enabled, the generated code will
diff --git a/devito/core/power.py b/devito/core/power.py
index ab651a1910..65cf4c3cf3 100644
--- a/devito/core/power.py
+++ b/devito/core/power.py
@@ -1,6 +1,10 @@
-from devito.core.cpu import Cpu64AdvCOperator, Cpu64AdvOmpOperator
+from devito.core.cpu import (Cpu64AdvCOperator, Cpu64AdvOmpOperator,
+                             Cpu64AdvCXXOperator, Cpu64AdvCXXOmpOperator)
 
-__all__ = ['PowerAdvCOperator', 'PowerAdvOmpOperator']
+__all__ = ['PowerAdvCOperator', 'PowerAdvOmpOperator',
+           'PowerCXXAdvCOperator', 'PowerAdvCXXOmpOperator']
 
 PowerAdvCOperator = Cpu64AdvCOperator
 PowerAdvOmpOperator = Cpu64AdvOmpOperator
+PowerCXXAdvCOperator = Cpu64AdvCXXOperator
+PowerAdvCXXOmpOperator = Cpu64AdvCXXOmpOperator
diff --git a/devito/data/allocators.py b/devito/data/allocators.py
index aff28ef108..4ccd7cddfc 100644
--- a/devito/data/allocators.py
+++ b/devito/data/allocators.py
@@ -1,6 +1,4 @@
 import abc
-from functools import reduce
-from operator import mul
 import ctypes
 from ctypes.util import find_library
 import mmap
@@ -11,7 +9,7 @@
 
 from devito.logger import logger
 from devito.parameters import configuration
-from devito.tools import dtype_to_ctype, is_integer
+from devito.tools import is_integer, infer_datasize
 
 __all__ = ['ALLOC_ALIGNED', 'ALLOC_NUMA_LOCAL', 'ALLOC_NUMA_ANY',
            'ALLOC_KNL_MCDRAM', 'ALLOC_KNL_DRAM', 'ALLOC_GUARD',
@@ -92,8 +90,7 @@ def initialize(cls):
         return
 
     def alloc(self, shape, dtype, padding=0):
-        datasize = int(reduce(mul, shape))
-        ctype = dtype_to_ctype(dtype)
+        ctype, datasize = infer_datasize(dtype, shape)
 
         # Add padding, if any
         try:
diff --git a/devito/finite_differences/differentiable.py b/devito/finite_differences/differentiable.py
index 2009f7a772..39458424d4 100644
--- a/devito/finite_differences/differentiable.py
+++ b/devito/finite_differences/differentiable.py
@@ -18,8 +18,7 @@
 from devito.logger import warning
 from devito.tools import (as_tuple, filter_ordered, flatten, frozendict,
                           infer_dtype, is_integer, split)
-from devito.types import (Array, DimensionTuple, Evaluable, Indexed,
-                          StencilDimension)
+from devito.types import Array, DimensionTuple, Evaluable, StencilDimension
 from devito.types.basic import AbstractFunction
 
 __all__ = ['Differentiable', 'DiffDerivative', 'IndexDerivative', 'EvalDerivative',
@@ -74,7 +73,7 @@ def grid(self):
 
     @cached_property
     def dtype(self):
-        dtypes = {f.dtype for f in self.find(Indexed)} - {None}
+        dtypes = {f.dtype for f in self._functions} - {None}
         return infer_dtype(dtypes)
 
     @cached_property
diff --git a/devito/ir/__init__.py b/devito/ir/__init__.py
index 0cfa5730eb..78a10d0a8c 100644
--- a/devito/ir/__init__.py
+++ b/devito/ir/__init__.py
@@ -2,3 +2,4 @@
 from devito.ir.equations import *  # noqa
 from devito.ir.clusters import *  # noqa
 from devito.ir.iet import *  # noqa
+from devito.ir.cgen import *  # noqa
\ No newline at end of file
diff --git a/devito/ir/cgen/__init__.py b/devito/ir/cgen/__init__.py
new file mode 100644
index 0000000000..b9d0e6ed2c
--- /dev/null
+++ b/devito/ir/cgen/__init__.py
@@ -0,0 +1 @@
+from devito.ir.cgen.printer import *  # noqa
\ No newline at end of file
diff --git a/devito/symbolics/printer.py b/devito/ir/cgen/printer.py
similarity index 52%
rename from devito/symbolics/printer.py
rename to devito/ir/cgen/printer.py
index 2c366a389e..cf6eee1a7c 100644
--- a/devito/symbolics/printer.py
+++ b/devito/ir/cgen/printer.py
@@ -1,28 +1,28 @@
 """
 Utilities to turn SymPy objects into C strings.
 """
-
 import numpy as np
 import sympy
 
 from mpmath.libmp import prec_to_dps, to_str
 from packaging.version import Version
-from numbers import Real
 
 from sympy.core import S
 from sympy.core.numbers import equal_valued, Float
+from sympy.printing.codeprinter import CodePrinter
 from sympy.logic.boolalg import BooleanFunction
 from sympy.printing.precedence import PRECEDENCE_VALUES, precedence
-from sympy.printing.c import C99CodePrinter
 
+from devito import configuration
 from devito.arch.compiler import AOMPCompiler
 from devito.symbolics.inspection import has_integer_args, sympy_dtype
 from devito.types.basic import AbstractFunction
+from devito.tools import ctypes_to_cstr, dtype_to_ctype, ctypes_vector_mapper
 
-__all__ = ['ccode']
+__all__ = ['BasePrinter', 'ccode']
 
 
-class CodePrinter(C99CodePrinter):
+class BasePrinter(CodePrinter):
 
     """
     Decorator for sympy.printing.ccode.CCodePrinter.
@@ -33,25 +33,97 @@ class CodePrinter(C99CodePrinter):
         Options for code printing.
     """
     _default_settings = {'compiler': None, 'dtype': np.float32,
-                         **C99CodePrinter._default_settings}
+                         **CodePrinter._default_settings}
+
+    _func_prefix = {}
+    _func_literals = {}
+    _prec_literals = {np.float32: 'F', np.complex64: 'F'}
+
+    _qualifiers_mapper = {
+        'is_const': 'const',
+        'is_volatile': 'volatile',
+        '_mem_constant': 'static',
+        '_mem_shared': '',
+    }
+
+    _restrict_keyword = 'restrict'
+
+    _includes = []
+    _namespaces = []
+    _headers = [('_POSIX_C_SOURCE', '200809L')]
 
     @property
     def dtype(self):
-        return self._settings['dtype']
+        try:
+            return self._settings['dtype'].nptype
+        except AttributeError:
+            return self._settings['dtype']
 
     @property
     def compiler(self):
-        return self._settings['compiler']
+        return self._settings['compiler'] or configuration['compiler']
+
+    def doprint(self, expr, assign_to=None):
+        """
+        The sympy code printer does a lot of extra things we do not need
+        as we handle all of it in the compiler so we directly default to `_print`.
+        """
+        return self._print(expr)
+
+    def _prec(self, expr):
+        dtype = sympy_dtype(expr, default=self.dtype)
+        if dtype is None or np.issubdtype(dtype, np.integer):
+            if any(isinstance(i, Float) for i in expr.atoms()):
+                try:
+                    return np.promote_types(self.dtype, np.float32).type
+                except np.exceptions.DTypePromotionError:
+                    # Corner cases, e.g. Void, cannot (shouldn't) be promoted
+                    return self.dtype
+            else:
+                return dtype or self.dtype
+        else:
+            return dtype or self.dtype
 
-    def single_prec(self, expr=None):
-        dtype = sympy_dtype(expr) if expr is not None else self.dtype
-        return dtype in [np.float32, np.float16]
+    def prec_literal(self, expr):
+        return self._prec_literals.get(self._prec(expr), '')
+
+    def func_literal(self, expr):
+        return self._func_literals.get(self._prec(expr), '')
+
+    def func_prefix(self, expr, mfunc=False):
+        prefix = self._func_prefix.get(self._prec(expr), '')
+        if mfunc:
+            return prefix
+        elif prefix == 'f':
+            return ''
+        else:
+            return prefix
 
     def parenthesize(self, item, level, strict=False):
         if isinstance(item, BooleanFunction):
-            return "(%s)" % self._print(item)
+            return f"({self._print(item)})"
         return super().parenthesize(item, level, strict=strict)
 
+    def _print_PyCPointerType(self, expr):
+        ctype = f'{self._print_type(expr._type_)}'
+        if ctype.endswith('*'):
+            return f'{ctype}*'
+        else:
+            return f'{ctype} *'
+
+    def _print_type(self, expr):
+        try:
+            expr = dtype_to_ctype(expr)
+        except TypeError:
+            pass
+        try:
+            return self.type_mappings[expr]
+        except KeyError:
+            return ctypes_to_cstr(expr)
+
+    def _print_VoidDType(self, expr):
+        return ctypes_vector_mapper[expr].__name__
+
     def _print_Function(self, expr):
         if isinstance(expr, AbstractFunction):
             return str(expr)
@@ -61,7 +133,7 @@ def _print_Function(self, expr):
             return super()._print_Function(expr)
 
     def _print_CondEq(self, expr):
-        return "%s == %s" % (self._print(expr.lhs), self._print(expr.rhs))
+        return f"{self._print(expr.lhs)} == {self._print(expr.rhs)}"
 
     def _print_Indexed(self, expr):
         """
@@ -72,7 +144,7 @@ def _print_Indexed(self, expr):
         U[t,x,y,z] -> U[t][x][y][z]
         """
         inds = ''.join(['[' + self._print(x) + ']' for x in expr.indices])
-        return '%s%s' % (self._print(expr.base.label), inds)
+        return f'{self._print(expr.base.label)}{inds}'
 
     def _print_FIndexed(self, expr):
         """
@@ -87,7 +159,7 @@ def _print_FIndexed(self, expr):
             label = expr.accessor.label
         except AttributeError:
             label = expr.base.label
-        return '%s(%s)' % (self._print(label), inds)
+        return f'{self._print(label)}({inds})'
 
     def _print_Rational(self, expr):
         """Print a Rational as a C-like float/float division."""
@@ -96,10 +168,8 @@ def _print_Rational(self, expr):
         # to be 32-bit floats.
         # http://en.cppreference.com/w/cpp/language/floating_literal
         p, q = int(expr.p), int(expr.q)
-        if self.dtype == np.float64:
-            return '%d.0/%d.0' % (p, q)
-        else:
-            return '%d.0F/%d.0F' % (p, q)
+        prec = self.prec_literal(expr)
+        return f'{p}.0{prec}/{q}.0{prec}'
 
     def _print_math_func(self, expr, nest=False, known=None):
         cls = type(expr)
@@ -113,8 +183,7 @@ def _print_math_func(self, expr, nest=False, known=None):
         if cname not in self._prec_funcs:
             return super()._print_math_func(expr, nest=nest, known=known)
 
-        if self.single_prec(expr):
-            cname = '%sf' % cname
+        cname = f'{self.func_prefix(expr)}{cname}{self.func_literal(expr)}'
 
         if nest and len(expr.args) > 2:
             args = ', '.join([self._print(expr.args[0]),
@@ -122,7 +191,7 @@ def _print_math_func(self, expr, nest=False, known=None):
         else:
             args = ', '.join([self._print(arg) for arg in expr.args])
 
-        return f'{cname}({args})'
+        return f'{self._ns}{cname}({args})'
 
     def _print_Pow(self, expr):
         # Completely reimplement `_print_Pow` from sympy, since it doesn't
@@ -130,16 +199,17 @@ def _print_Pow(self, expr):
         if "Pow" in self.known_functions:
             return self._print_Function(expr)
         PREC = precedence(expr)
-        suffix = 'f' if self.single_prec(expr) else ''
+        suffix = self.func_literal(expr)
+        base = self._print(expr.base)
         if equal_valued(expr.exp, -1):
             return self._print_Float(Float(1.0)) + '/' + \
                 self.parenthesize(expr.base, PREC)
         elif equal_valued(expr.exp, 0.5):
-            return f'sqrt{suffix}({self._print(expr.base)})'
+            return f'{self._ns}sqrt{suffix}({base})'
         elif expr.exp == S.One/3 and self.standard != 'C89':
-            return f'cbrt{suffix}({self._print(expr.base)})'
+            return f'{self._ns}cbrt{suffix}({base})'
         else:
-            return f'pow{suffix}({self._print(expr.base)}, {self._print(expr.exp)})'
+            return f'{self._ns}pow{suffix}({base}, {self._print(expr.exp)})'
 
     def _print_SafeInv(self, expr):
         """Print a SafeInv as a C-like division with a check for zero."""
@@ -149,42 +219,57 @@ def _print_SafeInv(self, expr):
 
     def _print_Mod(self, expr):
         """Print a Mod as a C-like %-based operation."""
-        args = ['(%s)' % self._print(a) for a in expr.args]
+        args = [f'({self._print(a)})' for a in expr.args]
         return '%'.join(args)
 
     def _print_Mul(self, expr):
-        term = super()._print_Mul(expr)
-        return term.replace("(-1)*", "-")
+        args = [a for a in expr.args if a != -1]
+        neg = (len(expr.args) - len(args)) % 2
+
+        if len(args) > 1:
+            term = super()._print_Mul(expr.func(*args, evaluate=False))
+        else:
+            term = self.parenthesize(args[0], precedence(expr))
+
+        if neg:
+            return f'-{term}'
+        else:
+            return term
+
+    def _print_fmath_func(self, name, expr):
+        args = ",".join([self._print(i) for i in expr.args])
+        func = f'{self.func_prefix(expr, mfunc=True)}{name}{self.func_literal(expr)}'
+        return f"{self._ns}{func}({args})"
 
     def _print_Min(self, expr):
-        if has_integer_args(*expr.args) and len(expr.args) == 2:
-            return "MIN(%s)" % self._print(expr.args)[1:-1]
+        if len(expr.args) > 2:
+            return self._print_Min(expr.func(expr.args[0],
+                                             expr.func(*expr.args[1:]),
+                                             evaluate=False))
+        elif has_integer_args(*expr.args) and len(expr.args) == 2:
+            return f"MIN({self._print(expr.args)[1:-1]})"
         else:
-            return super()._print_Min(expr)
+            return self._print_fmath_func('min', expr)
 
     def _print_Max(self, expr):
-        if has_integer_args(*expr.args) and len(expr.args) == 2:
-            return "MAX(%s)" % self._print(expr.args)[1:-1]
+        if len(expr.args) > 2:
+            return self._print_Max(expr.func(expr.args[0],
+                                             expr.func(*expr.args[1:]),
+                                             evaluate=False))
+        elif has_integer_args(*expr.args) and len(expr.args) == 2:
+            return f"MAX({self._print(expr.args)[1:-1]})"
         else:
-            return super()._print_Max(expr)
+            return self._print_fmath_func('max', expr)
 
     def _print_Abs(self, expr):
         """Print an absolute value. Use `abs` if can infer it is an Integer"""
+        # Unary function, single argument
+        arg = expr.args[0]
         # AOMPCC errors with abs, always use fabs
-        if isinstance(self.compiler, AOMPCompiler):
-            return "fabs(%s)" % self._print(expr.args[0])
-        # Check if argument is an integer
-        if has_integer_args(*expr.args[0].args):
-            func = "abs"
-        elif self.single_prec(expr):
-            func = "fabsf"
-        elif any([isinstance(a, Real) for a in expr.args[0].args]):
-            # The previous condition isn't sufficient to detect case with
-            # Python `float`s in that case, fall back to the "default"
-            func = "fabsf" if self.single_prec() else "fabs"
-        else:
-            func = "fabs"
-        return f"{func}({self._print(expr.args[0])})"
+        if isinstance(self.compiler, AOMPCompiler) and \
+           not np.issubdtype(self._prec(expr), np.integer):
+            return f"fabs({self._print(arg)})"
+        return self._print_fmath_func('abs', expr)
 
     def _print_Add(self, expr, order=None):
         """"
@@ -197,7 +282,7 @@ def _print_Add(self, expr, order=None):
         for term in terms:
             t = self._print(term)
             if precedence(term) < PREC:
-                l.extend(["+", "(%s)" % t])
+                l.extend(["+", f"({t})"])
             elif t.startswith('-'):
                 l.extend(["-", t[1:]])
             else:
@@ -234,75 +319,77 @@ def _print_Float(self, expr):
         if 'e' not in rv:
             rv = rv.rstrip('0') + "0"
 
-        if self.single_prec():
-            rv = '%sF' % rv
-
-        return rv
+        return f'{rv}{self.prec_literal(expr)}'
 
     def _print_Differentiable(self, expr):
-        return "(%s)" % self._print(expr._expr)
+        return f"({self._print(expr._expr)})"
 
-    _print_EvalDerivative = C99CodePrinter._print_Add
+    _print_EvalDerivative = _print_Add
 
     def _print_CallFromPointer(self, expr):
         indices = [self._print(i) for i in expr.params]
-        return "%s->%s(%s)" % (expr.pointer, expr.call, ', '.join(indices))
+        return f"{expr.pointer}->{expr.call}({', '.join(indices)})"
 
     def _print_CallFromComposite(self, expr):
         indices = [self._print(i) for i in expr.params]
-        return "%s.%s(%s)" % (expr.pointer, expr.call, ', '.join(indices))
+        return f"{expr.pointer}.{expr.call}({', '.join(indices)})"
 
     def _print_FieldFromPointer(self, expr):
-        return "%s->%s" % (expr.pointer, expr.field)
+        return f"{expr.pointer}->{expr.field}"
 
     def _print_FieldFromComposite(self, expr):
-        return "%s.%s" % (expr.pointer, expr.field)
+        return f"{expr.pointer}.{expr.field}"
 
     def _print_ListInitializer(self, expr):
-        return "{%s}" % ', '.join([self._print(i) for i in expr.params])
+        return f"{{{', '.join(self._print(i) for i in expr.params)}}}"
 
     def _print_IndexedPointer(self, expr):
-        return "%s%s" % (expr.base, ''.join('[%s]' % self._print(i) for i in expr.index))
+        return f"{expr.base}{''.join(f'[{self._print(i)}]' for i in expr.index)}"
 
     def _print_IntDiv(self, expr):
         lhs = self._print(expr.lhs)
         if not expr.lhs.is_Atom:
-            lhs = '(%s)' % (lhs)
+            lhs = f"({lhs})"
         rhs = self._print(expr.rhs)
         PREC = precedence(expr)
-        return self.parenthesize("%s / %s" % (lhs, rhs), PREC)
+        return self.parenthesize(f"{lhs} / {rhs}", PREC)
 
     def _print_InlineIf(self, expr):
         cond = self._print(expr.cond)
         true_expr = self._print(expr.true_expr)
         false_expr = self._print(expr.false_expr)
         PREC = precedence(expr)
-        return self.parenthesize("(%s) ? %s : %s" % (cond, true_expr, false_expr), PREC)
+        return self.parenthesize(f"({cond}) ? {true_expr} : {false_expr}", PREC)
 
-    def _print_UnaryOp(self, expr):
-        if expr.base.is_Symbol:
-            return "%s%s" % (expr._op, self._print(expr.base))
-        else:
-            return "%s(%s)" % (expr._op, self._print(expr.base))
+    def _print_UnaryOp(self, expr, op=None, parenthesize=False):
+        op = op or expr._op
+        base = self._print(expr.base)
+        if not expr.base.is_Symbol or parenthesize:
+            base = f'({base})'
+        return f'{op}{base}'
 
-    def _print_ComponentAccess(self, expr):
-        return "%s.%s" % (self._print(expr.base), expr.sindex)
+    def _print_Cast(self, expr):
+        cast = f'({self._print(expr._C_ctype)}{self._print(expr.stars)})'
+        return self._print_UnaryOp(expr, op=cast)
 
-    def _print_TrigonometricFunction(self, expr):
-        func_name = str(expr.func)
-        if self.single_prec():
-            func_name = '%sf' % func_name
-        return '%s(%s)' % (func_name, self._print(*expr.args))
+    def _print_ComponentAccess(self, expr):
+        return f"{self._print(expr.base)}.{expr.sindex}"
 
     def _print_DefFunction(self, expr):
         arguments = [self._print(i) for i in expr.arguments]
         if expr.template:
-            template = '<%s>' % ','.join([str(i) for i in expr.template])
+            ctemplate = ','.join([str(i) for i in expr.template])
+            template = f'<{ctemplate}>'
         else:
             template = ''
-        return "%s%s(%s)" % (expr.name, template, ','.join(arguments))
+        args = ','.join(arguments)
+        return f"{expr.name}{template}({args})"
 
-    _print_MathFunction = _print_DefFunction
+    def _print_SizeOf(self, expr):
+        return f'sizeof({self._print(expr.intype)}{self._print(expr.stars)})'
+
+    def _print_MathFunction(self, expr):
+        return f"{self._ns}{self._print_DefFunction(expr)}"
 
     def _print_Fallback(self, expr):
         return expr.__str__()
@@ -318,7 +405,7 @@ def _print_Fallback(self, expr):
 
 # Lifted from SymPy so that we go through our own `_print_math_func`
 for k in ('exp log sin cos tan ceiling floor').split():
-    setattr(CodePrinter, '_print_%s' % k, CodePrinter._print_math_func)
+    setattr(BasePrinter, f'_print_{k}', BasePrinter._print_math_func)
 
 
 # Always parenthesize IntDiv and InlineIf within expressions
@@ -326,7 +413,13 @@ def _print_Fallback(self, expr):
 PRECEDENCE_VALUES['InlineIf'] = 1
 
 
-def ccode(expr, **settings):
+# Sympy 1.11 has introduced a bug in `_print_Add`, so we enforce here
+# to always use the correct one from our printer
+if Version(sympy.__version__) >= Version("1.11"):
+    setattr(sympy.printing.str.StrPrinter, '_print_Add', BasePrinter._print_Add)
+
+
+def ccode(expr, printer=None, **settings):
     """Generate C++ code from an expression.
 
     Parameters
@@ -342,10 +435,7 @@ def ccode(expr, **settings):
         The resulting code as a C++ string. If something went south, returns
         the input ``expr`` itself.
     """
-    return CodePrinter(settings=settings).doprint(expr, None)
-
-
-# Sympy 1.11 has introduced a bug in `_print_Add`, so we enforce here
-# to always use the correct one from our printer
-if Version(sympy.__version__) >= Version("1.11"):
-    setattr(sympy.printing.str.StrPrinter, '_print_Add', CodePrinter._print_Add)
+    if printer is None:
+        from devito.passes.iet.languages.C import CPrinter
+        printer = CPrinter
+    return printer(settings=settings).doprint(expr, None)
diff --git a/devito/ir/iet/nodes.py b/devito/ir/iet/nodes.py
index d45c9af939..60646066b7 100644
--- a/devito/ir/iet/nodes.py
+++ b/devito/ir/iet/nodes.py
@@ -1,6 +1,7 @@
 """The Iteration/Expression Tree (IET) hierarchy."""
 
 import abc
+import ctypes
 import inspect
 from functools import cached_property
 from collections import OrderedDict, namedtuple
@@ -10,11 +11,12 @@
 from sympy import IndexedBase, sympify
 
 from devito.data import FULL
+from devito.ir.cgen import ccode
 from devito.ir.equations import DummyEq, OpInc, OpMin, OpMax
 from devito.ir.support import (INBOUND, SEQUENTIAL, PARALLEL, PARALLEL_IF_ATOMIC,
                                PARALLEL_IF_PVT, VECTORIZED, AFFINE, Property,
                                Forward, WithLock, PrefetchUpdate, detect_io)
-from devito.symbolics import ListInitializer, CallFromPointer, ccode
+from devito.symbolics import ListInitializer, CallFromPointer
 from devito.tools import (Signer, as_tuple, filter_ordered, filter_sorted, flatten,
                           ctypes_to_cstr)
 from devito.types.basic import (AbstractFunction, AbstractSymbol, Basic, Indexed,
@@ -28,7 +30,7 @@
            'Increment', 'Return', 'While', 'ListMajor', 'ParallelIteration',
            'ParallelBlock', 'Dereference', 'Lambda', 'SyncSpot', 'Pragma',
            'DummyExpr', 'BlankLine', 'ParallelTree', 'BusyWait', 'UsingNamespace',
-           'CallableBody', 'Transfer']
+           'Using', 'CallableBody', 'Transfer']
 
 # First-class IET nodes
 
@@ -62,12 +64,6 @@ class Node(Signer):
     appears in this list are treated as traversable fields.
     """
 
-    _ccode_handler = None
-    """
-    Customizable by subclasses, in particular Operator subclasses which define
-    backend-specific nodes and, as such, require node-specific handlers.
-    """
-
     def __new__(cls, *args, **kwargs):
         obj = super().__new__(cls)
         argnames, _, _, defaultvalues, _, _, _ = inspect.getfullargspec(cls.__init__)
@@ -152,7 +148,7 @@ def writes(self):
         return ()
 
     def _signature_items(self):
-        return (str(self.ccode),)
+        return (str(self),)
 
 
 class ExprStmt:
@@ -1043,6 +1039,8 @@ class Dereference(ExprStmt, Node):
         * `pointer` is a PointerArray or TempFunction, and `pointee` is an Array.
         * `pointer` is an ArrayObject representing a pointer to a C struct, and
           `pointee` is a field in `pointer`.
+        * `pointer` is a Symbol with its _C_ctype deriving from ct._Pointer, and
+          `pointee` is a Symbol representing the dereferenced value.
     """
 
     is_Dereference = True
@@ -1061,13 +1059,18 @@ def functions(self):
 
     @property
     def expr_symbols(self):
-        ret = [self.pointer.indexed]
-        if self.pointer.is_PointerArray or self.pointer.is_TempFunction:
-            ret.append(self.pointee.indexed)
-            ret.extend(flatten(i.free_symbols for i in self.pointee.symbolic_shape[1:]))
+        ret = []
+        if self.pointer.is_Symbol:
+            assert issubclass(self.pointer._C_ctype, ctypes._Pointer), \
+                   "Scalar dereference must have a pointer ctype"
+            ret.extend([self.pointer._C_symbol, self.pointee._C_symbol])
+        elif self.pointer.is_PointerArray or self.pointer.is_TempFunction:
+            ret.extend([self.pointer.indexed, self.pointee.indexed])
+            ret.extend(flatten(i.free_symbols
+                               for i in self.pointee.symbolic_shape[1:]))
             ret.extend(self.pointer.free_symbols)
         else:
-            ret.append(self.pointee._C_symbol)
+            ret.extend([self.pointer.indexed, self.pointee._C_symbol])
         return tuple(filter_ordered(ret))
 
     @property
@@ -1214,6 +1217,19 @@ def periodic(self):
         return self._periodic
 
 
+class Using(Node):
+
+    """
+    A C++ using directive.
+    """
+
+    def __init__(self, name):
+        self.name = name
+
+    def __repr__(self):
+        return "<Using(%s)>" % self.name
+
+
 class UsingNamespace(Node):
 
     """
diff --git a/devito/ir/iet/utils.py b/devito/ir/iet/utils.py
index a31c82973a..0ffe7c3d36 100644
--- a/devito/ir/iet/utils.py
+++ b/devito/ir/iet/utils.py
@@ -1,3 +1,5 @@
+import numpy as np
+
 from devito.ir.iet import FindSections, FindSymbols
 from devito.symbolics import Keyword, Macro
 from devito.tools import filter_ordered
@@ -166,3 +168,20 @@ def maybe_alias(obj, candidate):
         # the __rkwargs__ except for e.g. the name
 
     return False
+
+
+def has_dtype(iet, dtype):
+    """
+    Check if the given IET has at least one symbol with the given dtype or
+    dtype kind.
+    """
+    for f in FindSymbols().visit(iet):
+        try:
+            # Check if the dtype matches exactly (dtype input)
+            # or matches the generic kind (dtype generic input)
+            if np.issubdtype(f.dtype, dtype) or f.dtype == dtype:
+                return True
+        except TypeError:
+            continue
+    else:
+        return False
diff --git a/devito/ir/iet/visitors.py b/devito/ir/iet/visitors.py
index 360d5253f5..3817c4e39d 100644
--- a/devito/ir/iet/visitors.py
+++ b/devito/ir/iet/visitors.py
@@ -18,11 +18,12 @@
                                  Call, Lambda, BlankLine, Section, ListMajor)
 from devito.ir.support.space import Backward
 from devito.symbolics import (FieldFromComposite, FieldFromPointer,
-                              ListInitializer, ccode, uxreplace)
-from devito.tools import (GenericVisitor, as_tuple, ctypes_to_cstr, filter_ordered,
+                              ListInitializer, uxreplace)
+from devito.symbolics.extended_dtypes import NoDeclStruct
+from devito.tools import (GenericVisitor, as_tuple, filter_ordered,
                           filter_sorted, flatten, is_external_ctype,
                           c_restrict_void_p, sorted_priority)
-from devito.types.basic import AbstractFunction, Basic
+from devito.types.basic import AbstractFunction, AbstractSymbol, Basic
 from devito.types import (ArrayObject, CompositeObject, Dimension, Pointer,
                           IndexedData, DeviceMap)
 
@@ -79,23 +80,24 @@ def indent(self):
         return '  ' * self._depth
 
     def visit_Node(self, o):
-        return self.indent + '<%s>' % o.__class__.__name__
+        return self.indent + f'<{o.__class__.__name__}>'
 
     def visit_Generable(self, o):
-        body = ' %s' % str(o) if self.verbose else ''
-        return self.indent + '<C.%s%s>' % (o.__class__.__name__, body)
+        body = f" {str(o) if self.verbose else ''}"
+        return self.indent + f'<C.{o.__class__.__name__}{body}>'
 
     def visit_Callable(self, o):
         self._depth += 1
         body = self._visit(o.children)
         self._depth -= 1
-        return self.indent + '<Callable %s>\n%s' % (o.name, body)
+        return self.indent + f'<Callable {o.name}>\n{body}'
 
     def visit_CallableBody(self, o):
         self._depth += 1
         body = [self._visit(o.init), self._visit(o.unpacks), self._visit(o.body)]
         self._depth -= 1
-        return self.indent + "%s\n%s" % (o.__repr__(), '\n'.join([i for i in body if i]))
+        cbody = '\n'.join([i for i in body if i])
+        return self.indent + f"{o.__repr__()}\n{cbody}"
 
     def visit_list(self, o):
         return ('\n').join([self._visit(i) for i in o])
@@ -110,43 +112,49 @@ def visit_List(self, o):
         else:
             body = [self._visit(o.body)]
         self._depth -= 1
-        return self.indent + "%s\n%s" % (o.__repr__(), '\n'.join(body))
+        cbody = '\n'.join(body)
+        return self.indent + f"{o.__repr__()}\n{cbody}"
 
     def visit_TimedList(self, o):
         self._depth += 1
         body = [self._visit(o.body)]
         self._depth -= 1
-        return self.indent + "%s\n%s" % (o.__repr__(), '\n'.join(body))
+        cbody = '\n'.join(body)
+        return self.indent + f"{o.__repr__()}\n{cbody}"
 
     def visit_Iteration(self, o):
         self._depth += 1
         body = self._visit(o.children)
         self._depth -= 1
         if self.verbose:
-            detail = '::%s::%s' % (o.index, o.limits)
+            detail = f'::{o.index}::{o.limits}'
             props = [str(i) for i in o.properties]
-            props = '[%s] ' % ','.join(props) if props else ''
+            if props:
+                cprops = ','.join(props)
+                props = f'[{cprops}] '
+            else:
+                props = ''
         else:
             detail, props = '', ''
-        return self.indent + "<%sIteration %s%s>\n%s" % (props, o.dim.name, detail, body)
+        return self.indent + f"<{props}Iteration {o.dim.name}{detail}>\n{body}"
 
     def visit_While(self, o):
         self._depth += 1
         body = self._visit(o.children)
         self._depth -= 1
-        return self.indent + "<While %s>\n%s" % (o.condition, body)
+        return self.indent + f"<While {o.condition}>\n{body}"
 
     def visit_Expression(self, o):
         if self.verbose:
-            body = "%s = %s" % (o.expr.lhs, o.expr.rhs)
-            return self.indent + "<Expression %s>" % body
+            body = f"{o.expr.lhs} = {o.expr.rhs}"
+            return self.indent + f"<Expression {body}>"
         else:
             return self.indent + str(o)
 
     def visit_AugmentedExpression(self, o):
         if self.verbose:
-            body = "%s %s= %s" % (o.expr.lhs, o.op, o.expr.rhs)
-            return self.indent + "<%s %s>" % (o.__class__.__name__, body)
+            body = f"{o.expr.lhs} {o.op}= {o.expr.rhs}"
+            return self.indent + f"<{o.__class__.__name__} {body}>"
         else:
             return self.indent + str(o)
 
@@ -154,7 +162,7 @@ def visit_HaloSpot(self, o):
         self._depth += 1
         body = self._visit(o.children)
         self._depth -= 1
-        return self.indent + "%s\n%s" % (o.__repr__(), body)
+        return self.indent + f"{o.__repr__()}\n{body}"
 
     def visit_Conditional(self, o):
         self._depth += 1
@@ -162,10 +170,9 @@ def visit_Conditional(self, o):
         self._depth -= 1
         if o.else_body:
             else_body = self._visit(o.else_body)
-            return self.indent + "<If %s>\n%s\n<Else>\n%s" % (o.condition,
-                                                              then_body, else_body)
+            return self.indent + f"<If {o.condition}>\n{then_body}\n<Else>\n{else_body}"
         else:
-            return self.indent + "<If %s>\n%s" % (o.condition, then_body)
+            return self.indent + f"<If {o.condition}>\n{then_body}"
 
 
 class CGen(Visitor):
@@ -174,19 +181,23 @@ class CGen(Visitor):
     Return a representation of the Iteration/Expression tree as a :module:`cgen` tree.
     """
 
-    def __init__(self, *args, compiler=None, **kwargs):
+    def __init__(self, *args, printer=None, **kwargs):
         super().__init__(*args, **kwargs)
-        self._compiler = compiler
-
-    # The following mappers may be customized by subclasses (that is,
-    # backend-specific CGen-erators)
-    _qualifiers_mapper = {
-        'is_const': 'const',
-        'is_volatile': 'volatile',
-        '_mem_constant': 'static',
-        '_mem_shared': '',
-    }
-    _restrict_keyword = 'restrict'
+        if printer is None:
+            from devito.passes.iet.languages.C import CPrinter
+            printer = CPrinter
+        self.printer = printer
+
+    def ccode(self, expr, **kwargs):
+        return self.printer(settings=kwargs).doprint(expr, None)
+
+    @property
+    def _qualifiers_mapper(self):
+        return self.printer._qualifiers_mapper
+
+    @property
+    def _restrict_keyword(self):
+        return self.printer._restrict_keyword
 
     def _gen_struct_decl(self, obj, masked=()):
         """
@@ -197,7 +208,8 @@ def _gen_struct_decl(self, obj, masked=()):
             while issubclass(ctype, ctypes._Pointer):
                 ctype = ctype._type_
 
-            if not issubclass(ctype, ctypes.Structure):
+            if not issubclass(ctype, ctypes.Structure) or \
+               issubclass(ctype, NoDeclStruct):
                 return None
         except TypeError:
             # E.g., `ctype` is of type `dtypes_lowering.CustomDtype`
@@ -221,9 +233,9 @@ def _gen_struct_decl(self, obj, masked=()):
             try:
                 entries.append(self._gen_value(i, 0, masked=('const',)))
             except AttributeError:
-                cstr = ctypes_to_cstr(ct)
+                cstr = self.ccode(ct)
                 if ct is c_restrict_void_p:
-                    cstr = '%srestrict' % cstr
+                    cstr = f'{cstr}{self._restrict_keyword}'
                 entries.append(c.Value(cstr, n))
 
         return c.Struct(ctype.__name__, entries)
@@ -243,24 +255,24 @@ def _gen_value(self, obj, mode=1, masked=()):
                       if getattr(obj.function, k, False) and v not in masked]
 
         if (obj._mem_stack or obj._mem_constant) and mode == 1:
-            strtype = obj._C_typedata
-            strshape = ''.join('[%s]' % ccode(i) for i in obj.symbolic_shape)
+            strtype = self.ccode(obj._C_typedata)
+            strshape = ''.join(f'[{self.ccode(i)}]' for i in obj.symbolic_shape)
         else:
-            strtype = ctypes_to_cstr(obj._C_ctype)
+            strtype = self.ccode(obj._C_ctype)
             strshape = ''
             if isinstance(obj, (AbstractFunction, IndexedData)) and mode >= 1:
                 if not obj._mem_stack:
-                    strtype = '%s%s' % (strtype, self._restrict_keyword)
+                    strtype = f'{strtype}{self._restrict_keyword}'
         strtype = ' '.join(qualifiers + [strtype])
 
         if obj.is_LocalObject and obj._C_modifier is not None and mode == 2:
             strtype += obj._C_modifier
 
         strname = obj._C_name
-        strobj = '%s%s' % (strname, strshape)
+        strobj = f'{strname}{strshape}'
 
         if obj.is_LocalObject and obj.cargs and mode == 1:
-            arguments = [ccode(i) for i in obj.cargs]
+            arguments = [self.ccode(i) for i in obj.cargs]
             strobj = MultilineCall(strobj, arguments, True)
 
         value = c.Value(strtype, strobj)
@@ -274,9 +286,9 @@ def _gen_value(self, obj, mode=1, masked=()):
         if obj.is_Array and obj.initvalue is not None and mode == 1:
             init = ListInitializer(obj.initvalue)
             if not obj._mem_constant or init.is_numeric:
-                value = c.Initializer(value, ccode(init))
+                value = c.Initializer(value, self.ccode(init))
         elif obj.is_LocalObject and obj.initvalue is not None and mode == 1:
-            value = c.Initializer(value, ccode(obj.initvalue))
+            value = c.Initializer(value, self.ccode(obj.initvalue))
 
         return value
 
@@ -310,7 +322,7 @@ def _args_call(self, args):
                 else:
                     ret.append(i._C_name)
             except AttributeError:
-                ret.append(ccode(i))
+                ret.append(self.ccode(i))
         return ret
 
     def _gen_signature(self, o, is_declaration=False):
@@ -387,19 +399,20 @@ def visit_tuple(self, o):
     def visit_PointerCast(self, o):
         f = o.function
         i = f.indexed
+        cstr = self.ccode(i._C_typedata)
 
         if f.is_PointerArray:
             # lvalue
-            lvalue = c.Value(i._C_typedata, '**%s' % f.name)
+            lvalue = c.Value(cstr, f'**{f.name}')
 
             # rvalue
             if isinstance(o.obj, ArrayObject):
-                v = '%s->%s' % (o.obj.name, f._C_name)
+                v = f'{o.obj.name}->{f._C_name}'
             elif isinstance(o.obj, IndexedData):
                 v = f._C_name
             else:
                 assert False
-            rvalue = '(%s**) %s' % (i._C_typedata, v)
+            rvalue = f'({cstr}**) {v}'
 
         else:
             # lvalue
@@ -408,12 +421,12 @@ def visit_PointerCast(self, o):
             else:
                 v = f.name
             if o.flat is None:
-                shape = ''.join("[%s]" % ccode(i) for i in o.castshape)
-                rshape = '(*)%s' % shape
-                lvalue = c.Value(i._C_typedata, '(*restrict %s)%s' % (v, shape))
+                shape = ''.join(f"[{self.ccode(i)}]" for i in o.castshape)
+                rshape = f'(*){shape}'
+                lvalue = c.Value(cstr, f'(*{self._restrict_keyword} {v}){shape}')
             else:
                 rshape = '*'
-                lvalue = c.Value(i._C_typedata, '*%s' % v)
+                lvalue = c.Value(cstr, f'*{v}')
             if o.alignment and f._data_alignment:
                 lvalue = c.AlignedAttribute(f._data_alignment, lvalue)
 
@@ -426,14 +439,14 @@ def visit_PointerCast(self, o):
                 else:
                     assert False
 
-                rvalue = '(%s %s) %s->%s' % (i._C_typedata, rshape, f._C_name, v)
+                rvalue = f'({cstr} {rshape}) {f._C_name}->{v}'
             else:
                 if isinstance(o.obj, Pointer):
                     v = o.obj.name
                 else:
                     v = f._C_name
 
-                rvalue = '(%s %s) %s' % (i._C_typedata, rshape, v)
+                rvalue = f'({cstr} {rshape}) {v}'
 
         return c.Initializer(lvalue, rvalue)
 
@@ -441,19 +454,21 @@ def visit_Dereference(self, o):
         a0, a1 = o.functions
         if a1.is_PointerArray or a1.is_TempFunction:
             i = a1.indexed
+            cstr = self.ccode(i._C_typedata)
             if o.flat is None:
-                shape = ''.join("[%s]" % ccode(i) for i in a0.symbolic_shape[1:])
-                rvalue = '(%s (*)%s) %s[%s]' % (i._C_typedata, shape, a1.name,
-                                                a1.dim.name)
-                lvalue = c.Value(i._C_typedata,
-                                 '(*restrict %s)%s' % (a0.name, shape))
+                shape = ''.join(f"[{self.ccode(i)}]" for i in a0.symbolic_shape[1:])
+                rvalue = f'({cstr} (*){shape}) {a1.name}[{a1.dim.name}]'
+                lvalue = c.Value(cstr, f'(*{self._restrict_keyword} {a0.name}){shape}')
             else:
-                rvalue = '(%s *) %s[%s]' % (i._C_typedata, a1.name, a1.dim.name)
-                lvalue = c.Value(i._C_typedata, '*restrict %s' % a0.name)
+                rvalue = f'({cstr} *) {a1.name}[{a1.dim.name}]'
+                lvalue = c.Value(cstr, f'*{self._restrict_keyword} {a0.name}')
             if a0._data_alignment:
                 lvalue = c.AlignedAttribute(a0._data_alignment, lvalue)
         else:
-            rvalue = '%s->%s' % (a1.name, a0._C_name)
+            if a1.is_Symbol:
+                rvalue = f'*{a1.name}'
+            else:
+                rvalue = f'{a1.name}->{a0._C_name}'
             lvalue = self._gen_value(a0, 0)
         return c.Initializer(lvalue, rvalue)
 
@@ -475,15 +490,15 @@ def visit_Break(self, o):
     def visit_Return(self, o):
         v = 'return'
         if o.value is not None:
-            v += ' %s' % o.value
+            v += f' {self.ccode(o.value)}'
         return c.Statement(v)
 
     def visit_Definition(self, o):
         return self._gen_value(o.function)
 
     def visit_Expression(self, o):
-        lhs = ccode(o.expr.lhs, dtype=o.dtype, compiler=self._compiler)
-        rhs = ccode(o.expr.rhs, dtype=o.dtype, compiler=self._compiler)
+        lhs = self.ccode(o.expr.lhs, dtype=o.dtype)
+        rhs = self.ccode(o.expr.rhs, dtype=o.dtype)
 
         if o.init:
             code = c.Initializer(self._gen_value(o.expr.lhs, 0), rhs)
@@ -496,9 +511,9 @@ def visit_Expression(self, o):
         return code
 
     def visit_AugmentedExpression(self, o):
-        c_lhs = ccode(o.expr.lhs, dtype=o.dtype, compiler=self._compiler)
-        c_rhs = ccode(o.expr.rhs, dtype=o.dtype, compiler=self._compiler)
-        code = c.Statement("%s %s= %s" % (c_lhs, o.op, c_rhs))
+        c_lhs = self.ccode(o.expr.lhs, dtype=o.dtype)
+        c_rhs = self.ccode(o.expr.rhs, dtype=o.dtype)
+        code = c.Statement(f"{c_lhs} {o.op}= {c_rhs}")
         if o.pragmas:
             code = c.Module(self._visit(o.pragmas) + (code,))
         return code
@@ -516,7 +531,7 @@ def visit_Call(self, o, nested_call=False):
                                  o.templates)
             if retobj.is_Indexed or \
                isinstance(retobj, (FieldFromComposite, FieldFromPointer)):
-                return c.Assign(ccode(retobj), call)
+                return c.Assign(self.ccode(retobj), call)
             else:
                 return c.Initializer(c.Value(rettype, retobj._C_name), call)
 
@@ -530,9 +545,9 @@ def visit_Conditional(self, o):
         then_body = c.Block(self._visit(then_body))
         if else_body:
             else_body = c.Block(self._visit(else_body))
-            return c.If(ccode(o.condition), then_body, else_body)
+            return c.If(self.ccode(o.condition), then_body, else_body)
         else:
-            return c.If(ccode(o.condition), then_body)
+            return c.If(self.ccode(o.condition), then_body)
 
     def visit_Iteration(self, o):
         body = flatten(self._visit(i) for i in self._blankline_logic(o.children))
@@ -542,23 +557,23 @@ def visit_Iteration(self, o):
 
         # For backward direction flip loop bounds
         if o.direction == Backward:
-            loop_init = 'int %s = %s' % (o.index, ccode(_max))
-            loop_cond = '%s >= %s' % (o.index, ccode(_min))
-            loop_inc = '%s -= %s' % (o.index, o.limits[2])
+            loop_init = f'int {o.index} = {self.ccode(_max)}'
+            loop_cond = f'{o.index} >= {self.ccode(_min)}'
+            loop_inc = f'{o.index} -= {o.limits[2]}'
         else:
-            loop_init = 'int %s = %s' % (o.index, ccode(_min))
-            loop_cond = '%s <= %s' % (o.index, ccode(_max))
-            loop_inc = '%s += %s' % (o.index, o.limits[2])
+            loop_init = f'int {o.index} = {self.ccode(_min)}'
+            loop_cond = f'{o.index} <= {self.ccode(_max)}'
+            loop_inc = f'{o.index} += {o.limits[2]}'
 
         # Append unbounded indices, if any
         if o.uindices:
-            uinit = ['%s = %s' % (i.name, ccode(i.symbolic_min)) for i in o.uindices]
+            uinit = [f'{i.name} = {self.ccode(i.symbolic_min)}' for i in o.uindices]
             loop_init = c.Line(', '.join([loop_init] + uinit))
 
             ustep = []
             for i in o.uindices:
                 op = '=' if i.is_Modulo else '+='
-                ustep.append('%s %s %s' % (i.name, op, ccode(i.symbolic_incr)))
+                ustep.append(f'{i.name} {op} {self.ccode(i.symbolic_incr)}')
             loop_inc = c.Line(', '.join([loop_inc] + ustep))
 
         # Create For header+body
@@ -575,13 +590,13 @@ def visit_Pragma(self, o):
         return c.Pragma(o._generate)
 
     def visit_While(self, o):
-        condition = ccode(o.condition)
+        condition = self.ccode(o.condition)
         if o.body:
             body = flatten(self._visit(i) for i in o.children)
             return c.While(condition, c.Block(body))
         else:
             # Hack: cgen doesn't support body-less while-loops, i.e. `while(...);`
-            return c.Statement('while(%s)' % condition)
+            return c.Statement(f'while({condition})')
 
     def visit_Callable(self, o):
         body = flatten(self._visit(i) for i in o.children)
@@ -600,8 +615,11 @@ def visit_MultiTraversable(self, o):
                 body.extend(as_tuple(v))
         return c.Collection(body)
 
+    def visit_Using(self, o):
+        return c.Statement(f'using {str(o.name)}')
+
     def visit_UsingNamespace(self, o):
-        return c.Statement('using namespace %s' % ccode(o.namespace))
+        return c.Statement(f'using namespace {str(o.namespace)}')
 
     def visit_Lambda(self, o):
         body = []
@@ -611,17 +629,19 @@ def visit_Lambda(self, o):
                 if body:
                     body.append(c.Line())
                 body.extend(as_tuple(v))
+
         captures = [str(i) for i in o.captures]
         decls = [i.inline() for i in self._args_decl(o.parameters)]
+
         extra = []
         if o.special:
             extra.append(' ')
             extra.append(' '.join(str(i) for i in o.special))
         if o.attributes:
             extra.append(' ')
-            extra.append(' '.join('[[%s]]' % i for i in o.attributes))
-        top = c.Line('[%s](%s)%s' %
-                     (', '.join(captures), ', '.join(decls), ''.join(extra)))
+            extra.append(' '.join(f'[[{i}]]' for i in o.attributes))
+
+        top = c.Line(f"[{', '.join(captures)}]({', '.join(decls)}){''.join(extra)}")
         return LambdaCollection([top, c.Block(body)])
 
     def visit_HaloSpot(self, o):
@@ -630,7 +650,7 @@ def visit_HaloSpot(self, o):
 
     def visit_KernelLaunch(self, o):
         if o.templates:
-            templates = '<%s>' % ','.join([str(i) for i in o.templates])
+            templates = f"<{','.join([str(i) for i in o.templates])}>"
         else:
             templates = ''
 
@@ -644,14 +664,34 @@ def visit_KernelLaunch(self, o):
         arguments = self._args_call(o.arguments)
         arguments = ','.join(arguments)
 
-        return c.Statement('%s%s<<<%s>>>(%s)'
-                           % (o.name, templates, launch_config, arguments))
+        return c.Statement(f'{o.name}{templates}<<<{launch_config}>>>({arguments})')
 
     # Operator-handle machinery
 
     def _operator_includes(self, o):
+        """
+        Generate cgen includes from an iterable of symbols and expressions.
+        """
         return [c.Include(i, system=(False if i.endswith('.h') else True))
-                for i in o._includes]
+                for i in o.includes] + [blankline]
+
+    def _operator_namespaces(self, o):
+        """
+        Generate cgen namespaces from an iterable of symbols and expressions.
+        """
+        namespaces = [self._visit(i) for i in o.namespaces]
+        if namespaces:
+            namespaces.append(blankline)
+        return namespaces
+
+    def _operator_headers(self, o):
+        """
+        Generate cgen headers from an iterable of symbols and expressions.
+        """
+        headers = [c.Define(*as_tuple(i)) for i in o.headers]
+        if headers:
+            headers.append(blankline)
+        return headers
 
     def _operator_typedecls(self, o, mode='all'):
         xfilter0 = lambda i: self._gen_struct_decl(i) is not None
@@ -709,15 +749,13 @@ def visit_Operator(self, o, mode='all'):
             efuncs.extend([self._visit(i), blankline])
 
         # Definitions
-        headers = [c.Define(*i) for i in o._headers] + [blankline]
+        headers = self._operator_headers(o)
 
         # Header files
-        includes = self._operator_includes(o) + [blankline]
+        includes = self._operator_includes(o)
 
         # Namespaces
-        namespaces = [self._visit(i) for i in o._namespaces]
-        if namespaces:
-            namespaces.append(blankline)
+        namespaces = self._operator_namespaces(o)
 
         # Type declarations
         typedecls = self._operator_typedecls(o, mode)
@@ -738,7 +776,7 @@ class CInterface(CGen):
 
     def _operator_includes(self, o):
         includes = super()._operator_includes(o)
-        includes.append(c.Include("%s.h" % o.name, system=False))
+        includes.append(c.Include(f"{o.name}.h", system=False))
 
         return includes
 
@@ -750,7 +788,7 @@ def visit_Operator(self, o):
         typedecls = self._operator_typedecls(o, mode='public')
         guarded_typedecls = []
         for i in typedecls:
-            guard = "DEVITO_%s" % i.tpname.upper()
+            guard = f"DEVITO_{i.tpname.upper()}"
             iflines = [c.Define(guard, ""), blankline, i, blankline]
             guarded_typedecl = c.IfNDef(guard, iflines, [])
             guarded_typedecls.extend([guarded_typedecl, blankline])
@@ -956,6 +994,7 @@ def default_retval(cls):
         Drive the search. Accepted:
         - `symbolics`: Collect all AbstractFunction objects, default
         - `basics`: Collect all Basic objects
+        - `abstractsymbols`: Collect all AbstractSymbol objects
         - `dimensions`: Collect all Dimensions
         - `indexeds`: Collect all Indexed objects
         - `indexedbases`: Collect all IndexedBase objects
@@ -976,6 +1015,8 @@ def _defines_aliases(n):
     rules = {
         'symbolics': lambda n: n.functions,
         'basics': lambda n: [i for i in n.expr_symbols if isinstance(i, Basic)],
+        'symbols': lambda n: [i for i in n.expr_symbols
+                              if isinstance(i, AbstractSymbol)],
         'dimensions': lambda n: [i for i in n.expr_symbols if isinstance(i, Dimension)],
         'indexeds': lambda n: [i for i in n.expr_symbols if i.is_Indexed],
         'indexedbases': lambda n: [i for i in n.expr_symbols
@@ -1376,13 +1417,14 @@ def __init__(self, name, arguments, is_expr=False, is_indirect=False,
 
     def generate(self):
         if self.templates:
-            tip = "%s<%s>" % (self.name, ", ".join(str(i) for i in self.templates))
+            tip = f"{self.name}<{', '.join(str(i) for i in self.templates)}>"
         else:
             tip = self.name
         if not self.is_indirect:
-            tip = "%s(" % tip
+            tip = f"{tip}("
         else:
-            tip = "%s%s" % (tip, ',' if self.arguments else '')
+            cargs = ',' if self.arguments else ''
+            tip = f"{tip}{cargs}"
         processed = []
         for i in self.arguments:
             if isinstance(i, (MultilineCall, LambdaCollection)):
@@ -1404,7 +1446,7 @@ def generate(self):
         if not self.is_expr:
             tip += ";"
         if self.cast:
-            tip = '(%s)%s' % (self.cast, tip)
+            tip = f'({self.cast}){tip}'
         yield tip
 
 
diff --git a/devito/mpi/routines.py b/devito/mpi/routines.py
index 8b4987c8bb..745a30df2a 100644
--- a/devito/mpi/routines.py
+++ b/devito/mpi/routines.py
@@ -15,8 +15,8 @@
                            Transformer, ElementalCall, CommCallable)
 from devito.mpi import MPI
 from devito.symbolics import (Byref, CondNe, FieldFromPointer, FieldFromComposite,
-                              IndexedPointer, Macro, cast_mapper, subs_op_args)
-from devito.tools import (as_mapper, dtype_to_mpitype, dtype_len, dtype_to_ctype,
+                              IndexedPointer, Macro, cast, subs_op_args)
+from devito.tools import (as_mapper, dtype_to_mpitype, dtype_len, infer_datasize,
                           flatten, generator, is_integer, split)
 from devito.types import (Array, Bag, Dimension, Eq, Symbol, LocalObject,
                           CompositeObject, CustomDimension)
@@ -605,7 +605,7 @@ def _make_msg(self, f, hse, key):
         return MPIMsg('msg%d' % key, f, halos)
 
     def _make_sendrecv(self, f, hse, key, msg=None):
-        cast = cast_mapper[(f.c0.dtype, '*')]
+        fcast = cast(f.c0.dtype, '*')
         comm = f.grid.distributor._obj_comm
 
         bufg = FieldFromPointer(msg._C_field_bufg, msg)
@@ -619,7 +619,7 @@ def _make_sendrecv(self, f, hse, key, msg=None):
         sizes = [FieldFromPointer('%s[%d]' % (msg._C_field_sizes, i), msg)
                  for i in range(len(f._dist_dimensions))]
 
-        arguments = [cast(bufg)] + sizes + list(f.handles) + ofsg
+        arguments = [fcast(bufg)] + sizes + list(f.handles) + ofsg
         gather = Gather('gather%s' % key, arguments)
         # The `gather` is unnecessary if sending to MPI.PROC_NULL
         gather = Conditional(CondNe(torank, Macro('MPI_PROC_NULL')), gather)
@@ -671,7 +671,7 @@ def _call_compute(self, hs, compute, *args):
             return compute.make_call(dynamic_args_mapper=hs.omapper.core)
 
     def _make_wait(self, f, hse, key, msg=None):
-        cast = cast_mapper[(f.c0.dtype, '*')]
+        fcast = cast(f.c0.dtype, '*')
 
         bufs = FieldFromPointer(msg._C_field_bufs, msg)
 
@@ -681,7 +681,7 @@ def _make_wait(self, f, hse, key, msg=None):
 
         sizes = [FieldFromPointer('%s[%d]' % (msg._C_field_sizes, i), msg)
                  for i in range(len(f._dist_dimensions))]
-        arguments = [cast(bufs)] + sizes + list(f.handles) + ofss
+        arguments = [fcast(bufs)] + sizes + list(f.handles) + ofss
         scatter = Scatter('scatter%s' % key, arguments)
 
         # The `scatter` must be guarded as we must not alter the halo values along
@@ -772,7 +772,7 @@ def _call_sendrecv(self, *args):
         return
 
     def _make_haloupdate(self, f, hse, key, *args, msg=None):
-        cast = cast_mapper[(f.c0.dtype, '*')]
+        fcast = cast(f.c0.dtype, '*')
         comm = f.grid.distributor._obj_comm
 
         fixed = {d: Symbol(name="o%s" % d.root) for d in hse.loc_indices}
@@ -794,7 +794,7 @@ def _make_haloupdate(self, f, hse, key, *args, msg=None):
         ofsg = [fixed.get(d) or ofsg.pop(0) for d in f.dimensions]
 
         # The `gather` is unnecessary if sending to MPI.PROC_NULL
-        arguments = [cast(bufg)] + sizes + list(f.handles) + ofsg
+        arguments = [fcast(bufg)] + sizes + list(f.handles) + ofsg
         gather = Gather('gather%s' % key, arguments)
         gather = Conditional(CondNe(torank, Macro('MPI_PROC_NULL')), gather)
 
@@ -819,7 +819,7 @@ def _call_haloupdate(self, name, f, hse, msg):
         return HaloUpdateCall(name, args)
 
     def _make_halowait(self, f, hse, key, *args, msg=None):
-        cast = cast_mapper[(f.c0.dtype, '*')]
+        fcast = cast(f.c0.dtype, '*')
 
         fixed = {d: Symbol(name="o%s" % d.root) for d in hse.loc_indices}
 
@@ -839,7 +839,7 @@ def _make_halowait(self, f, hse, key, *args, msg=None):
 
         # The `scatter` must be guarded as we must not alter the halo values along
         # the domain boundary, where the sender is actually MPI.PROC_NULL
-        arguments = [cast(bufs)] + sizes + list(f.handles) + ofss
+        arguments = [fcast(bufs)] + sizes + list(f.handles) + ofss
         scatter = Scatter('scatter%s' % key, arguments)
         scatter = Conditional(CondNe(fromrank, Macro('MPI_PROC_NULL')), scatter)
 
@@ -1204,8 +1204,8 @@ def _arg_defaults(self, allocator, alias, args=None):
             entry.sizes = (c_int*len(shape))(*shape)
 
             # Allocate the send/recv buffers
-            size = reduce(mul, shape)*dtype_len(self.target.dtype)
-            ctype = dtype_to_ctype(f.dtype)
+            ctype, datasize = infer_datasize(f.dtype, shape)
+            size = datasize * dtype_len(self.target.dtype)
             entry.bufg, bufg_memfree_args = allocator._alloc_C_libcall(size, ctype)
             entry.bufs, bufs_memfree_args = allocator._alloc_C_libcall(size, ctype)
 
diff --git a/devito/operator/operator.py b/devito/operator/operator.py
index 57b29e90de..0a9f9db62f 100644
--- a/devito/operator/operator.py
+++ b/devito/operator/operator.py
@@ -27,6 +27,7 @@
 from devito.passes import (Graph, lower_index_derivatives, generate_implicit,
                            generate_macros, minimize_symbols, unevaluate,
                            error_mapper, is_on_device)
+from devito.passes.iet.dtypes import lower_dtypes
 from devito.symbolics import estimate_cost, subs_op_args
 from devito.tools import (DAG, OrderedSet, Signer, ReducerMap, as_mapper, as_tuple,
                           flatten, filter_sorted, frozendict, is_integer,
@@ -143,11 +144,6 @@ class Operator(Callable):
     refer to the relevant documentation.
     """
 
-    _default_headers = [('_POSIX_C_SOURCE', '200809L')]
-    _default_includes = ['stdlib.h', 'math.h', 'sys/time.h']
-    _default_globals = []
-    _default_namespaces = []
-
     def __new__(cls, expressions, **kwargs):
         if expressions is None:
             # Return a dummy Callable. This is exploited by unpickling. Users
@@ -207,15 +203,11 @@ def _build(cls, expressions, **kwargs):
         Callable.__init__(op, **op.args)
 
         # Header files, etc.
-        op._headers = OrderedSet(*cls._default_headers)
-        op._headers.update(byproduct.headers)
-        op._globals = OrderedSet(*cls._default_globals)
-        op._globals.update(byproduct.globals)
-        op._includes = OrderedSet(*cls._default_includes)
-        op._includes.update(profiler._default_includes)
+        op._headers = OrderedSet(*byproduct.headers)
+        op._globals = OrderedSet(*byproduct.globals)
+        op._includes = OrderedSet(*profiler._default_includes)
         op._includes.update(byproduct.includes)
-        op._namespaces = OrderedSet(*cls._default_namespaces)
-        op._namespaces.update(byproduct.namespaces)
+        op._namespaces = OrderedSet(*byproduct.namespaces)
 
         # Required for the jit-compilation
         op._compiler = kwargs['compiler']
@@ -268,6 +260,9 @@ def _lower(cls, expressions, **kwargs):
         """
         # Create a symbol registry
         kwargs.setdefault('sregistry', SymbolRegistry())
+        # Add lang-base kwargs
+        kwargs.setdefault('langbb', cls._Target.langbb())
+        kwargs.setdefault('printer', cls._Target.Printer)
 
         expressions = as_tuple(expressions)
 
@@ -493,6 +488,9 @@ def _lower_iet(cls, uiet, profiler=None, **kwargs):
         # Extract the necessary macros from the symbolic objects
         generate_macros(graph, **kwargs)
 
+        # Add type specific metadata
+        lower_dtypes(graph, **kwargs)
+
         # Target-independent optimizations
         minimize_symbols(graph)
 
@@ -766,13 +764,26 @@ def _soname(self):
         """A unique name for the shared object resulting from JIT compilation."""
         return Signer._digest(self, configuration)
 
+    @cached_property
+    def _printer(self):
+        return self._Target.Printer
+
+    @cached_property
+    def headers(self):
+        return OrderedSet(*self._printer._headers).union(self._headers)
+
+    @cached_property
+    def includes(self):
+        return OrderedSet(*self._printer._includes).union(self._includes)
+
+    @cached_property
+    def namespaces(self):
+        return OrderedSet(*self._printer._namespaces).union(self._namespaces)
+
     @cached_property
     def ccode(self):
-        try:
-            return self._ccode_handler(compiler=self._compiler).visit(self)
-        except (AttributeError, TypeError):
-            from devito.ir.iet.visitors import CGen
-            return CGen(compiler=self._compiler).visit(self)
+        from devito.ir.iet.visitors import CGen
+        return CGen(printer=self._printer).visit(self)
 
     def _jit_compile(self):
         """
@@ -787,11 +798,11 @@ def _jit_compile(self):
 
             elapsed = self._profiler.py_timers['jit-compile']
             if recompiled:
-                perf("Operator `%s` jit-compiled `%s` in %.2f s with `%s`" %
-                     (self.name, src_file, elapsed, self._compiler))
+                perf(f"Operator `{self.name}` jit-compiled `{src_file}` in "
+                     f"{elapsed:.2f} s with `{self._compiler}`")
             else:
-                perf("Operator `%s` fetched `%s` in %.2f s from jit-cache" %
-                     (self.name, src_file, elapsed))
+                perf(f"Operator `{self.name}` fetched `{src_file}` in "
+                     f"{elapsed:.2f} s from jit-cache")
 
     @property
     def cfunction(self):
@@ -825,7 +836,7 @@ def cinterface(self, force=False):
         dest = self._compiler.get_jit_dir()
         name = dest.joinpath(self.name)
 
-        cfile = name.with_suffix(".%s" % self._compiler.src_ext)
+        cfile = name.with_suffix(f".{self._compiler.src_ext}")
         hfile = name.with_suffix('.h')
 
         # Generate the .c and .h code
@@ -833,11 +844,11 @@ def cinterface(self, force=False):
 
         for f, code in [(cfile, ccode), (hfile, hcode)]:
             if not force and f.is_file():
-                debug("`%s` was not saved in `%s` as it already exists" % (f.name, dest))
+                debug(f"`{f.name}` was not saved in `{dest}` as it already exists")
             else:
                 with open(str(f), 'w') as ff:
                     ff.write(str(code))
-                debug("`%s` successfully saved in `%s`" % (f.name, dest))
+                debug(f"`{f.name}` successfully saved in `{dest}`")
 
         return ccode, hcode
 
@@ -954,7 +965,7 @@ def _emit_build_profiling(self):
         timings = self._profiler.py_timers.copy()
 
         tot = timings.pop('op-compile')
-        perf("Operator `%s` generated in %.2f s" % (self.name, fround(tot)))
+        perf(f"Operator `{self.name}` generated in {fround(tot):.2f} s")
 
         max_hotspots = 3
         threshold = 20.
@@ -966,14 +977,14 @@ def _emit_timings(timings, indent=''):
                 v = fround(timings[i]['total'])
                 perc = fround(v/tot*100, n=10)
                 if perc > threshold:
-                    perf("%s%s: %.2f s (%.1f %%)" % (indent, i.lstrip('_'), v, perc))
+                    perf(f"{indent}{i.lstrip('_')}: {v:.2f} s ({perc:.1f} %)")
                     _emit_timings(timings[i], ' '*len(indent) + ' * ')
 
         _emit_timings(timings, '  * ')
 
         if self._profiler._ops:
             ops = ['%d --> %d' % i for i in self._profiler._ops]
-            perf("Flops reduction after symbolic optimization: [%s]" % ' ; '.join(ops))
+            perf(f"Flops reduction after symbolic optimization: [{' ; '.join(ops)}]")
 
     def _emit_apply_profiling(self, args):
         """Produce a performance summary of the profiled sections."""
@@ -981,7 +992,7 @@ def _emit_apply_profiling(self, args):
         fround = lambda i: ceil(i * 100) / 100
 
         elapsed = fround(self._profiler.py_timers['apply'])
-        info("Operator `%s` ran in %.2f s" % (self.name, elapsed))
+        info(f"Operator `{self.name}` ran in {elapsed:.2f} s")
 
         summary = self._profiler.summary(args, self._dtype, reduce_over=elapsed)
 
@@ -999,16 +1010,16 @@ def _emit_apply_profiling(self, args):
             v = summary.globals.get('vanilla')
             if v is not None:
                 if v.oi is not None:
-                    metrics.append("OI=%.2f" % fround(v.oi))
+                    metrics.append(f"OI={fround(v.oi):.2f}")
                 if v.gflopss is not None and np.isfinite(v.gflopss):
-                    metrics.append("%.2f GFlops/s" % fround(v.gflopss))
+                    metrics.append(f"{fround(v.gflopss):.2f} GFlops/s")
 
             v = summary.globals.get('fdlike')
             if v is not None:
-                metrics.append("%.2f GPts/s" % fround(v.gpointss))
+                metrics.append(f"{fround(v.gpointss):.2f} GPts/s")
 
             if metrics:
-                perf("Global performance: [%s]" % ', '.join(metrics))
+                perf(f"Global performance: [{', '.join(metrics)}]")
 
             # Same as above, but excluding the setup phase, e.g. the CPU-GPU
             # data transfers in the case of a GPU run, mallocs, frees, etc.
@@ -1016,10 +1027,10 @@ def _emit_apply_profiling(self, args):
 
             v = summary.globals.get('fdlike-nosetup')
             if v is not None:
-                metrics.append("%.2f s" % fround(v.time))
-                metrics.append("%.2f GPts/s" % fround(v.gpointss))
+                metrics.append(f"{fround(v.time):.2f} s")
+                metrics.append(f"{fround(v.gpointss):.2f} GPts/s")
 
-                perf("Global performance <w/o setup>: [%s]" % ', '.join(metrics))
+                perf(f"Global performance <w/o setup>: [{', '.join(metrics)}]")
 
             # Prepare for the local performance indicators
             perf("Local performance:")
@@ -1032,34 +1043,33 @@ def _emit_apply_profiling(self, args):
         def lower_perfentry(v):
             values = []
             if v.oi:
-                values.append("OI=%.2f" % fround(v.oi))
+                values.append(f"OI={fround(v.oi):.2f}")
             if v.gflopss:
-                values.append("%.2f GFlops/s" % fround(v.gflopss))
+                values.append(f"{fround(v.gflopss):.2f} GFlops/s")
             if v.gpointss:
-                values.append("%.2f GPts/s" % fround(v.gpointss))
+                values.append(f"{fround(v.gpointss):.2f} GPts/s")
 
             if values:
-                return "[%s]" % ", ".join(values)
+                return f"[{', '.join(values)}]"
             else:
                 return ""
 
         for k, v in summary.items():
-            rank = "[rank%d]" % k.rank if k.rank is not None else ""
-            name = "%s%s" % (k.name, rank)
+            rank = f"[rank{k.rank if k.rank is not None else ''}]"
+            name = f"{k.name}{rank}"
 
             if v.time <= 0.01:
                 # Trim down the output for very fast sections
-                perf("%s* %s ran in %.2f s" % (indent, name, fround(v.time)))
+                perf(f"{indent}* {name} ran in {fround(v.time):.2f} s")
                 continue
 
             metrics = lower_perfentry(v)
-            perf("%s* %s ran in %.2f s %s" % (indent, name, fround(v.time), metrics))
+            perf(f"{indent}* {name} ran in {fround(v.time):.2f} s {metrics}")
             for n, v1 in summary.subsections.get(k.name, {}).items():
                 metrics = lower_perfentry(v1)
 
-                perf("%s+ %s ran in %.2f s [%.2f%%] %s" %
-                     (indent*2, n, fround(v1.time), fround(v1.time/v.time*100),
-                      metrics))
+                perf(f"{indent*2}+ {n} ran in {fround(v1.time):.2f} s "
+                     f"[{fround(v1.time/v.time*100):.2f}%] {metrics}")
 
         # Emit performance mode and arguments
         perf_args = {}
@@ -1077,7 +1087,7 @@ def lower_perfentry(v):
         if is_integer(self.npthreads):
             perf_args['pthreads'] = self.npthreads
         perf_args = {k: perf_args[k] for k in sorted(perf_args)}
-        perf("Performance[mode=%s] arguments: %s" % (self._mode, perf_args))
+        perf(f"Performance[mode={self._mode}] arguments: {perf_args}")
 
         return summary
 
@@ -1128,7 +1138,7 @@ def __setstate__(self, state):
             self._lib.name = soname
 
         self._allocator = default_allocator(
-            '%s.%s.%s' % (self._compiler.name, self._language, self._platform)
+            f'{type(self._compiler).__name__}.{self._language}.{self._platform}'
         )
 
 
@@ -1318,7 +1328,7 @@ def parse_kwargs(**kwargs):
             warning("Both `dle` and `opt` were passed; ignoring `dle` argument")
             opt = kwargs.pop('opt')
         else:
-            warning("Setting `opt=%s`" % str(dle))
+            warning(f"Setting `opt={str(dle)}`")
             opt = dle
     elif 'opt' in kwargs:
         opt = kwargs.pop('opt')
@@ -1338,7 +1348,7 @@ def parse_kwargs(**kwargs):
         else:
             mode, options = tuple(flatten(i.split(',') for i in opt)), {}
     else:
-        raise InvalidOperator("Illegal `opt=%s`" % str(opt))
+        raise InvalidOperator(f"Illegal `opt={str(opt)}`")
 
     # `opt`, deprecated kwargs
     kwopenmp = kwargs.get('openmp', options.get('openmp'))
@@ -1358,7 +1368,7 @@ def parse_kwargs(**kwargs):
     for i in deprecated_options:
         try:
             options.pop(i)
-            warning("Ignoring deprecated optimization option `%s`" % i)
+            warning(f"Ignoring deprecated optimization option `{i}`")
         except KeyError:
             pass
     kwargs['options'] = options
@@ -1374,7 +1384,7 @@ def parse_kwargs(**kwargs):
         if not isinstance(platform, str):
             raise ValueError("Argument `platform` should be a `str`")
         if platform not in configuration._accepted['platform']:
-            raise InvalidOperator("Illegal `platform=%s`" % str(platform))
+            raise InvalidOperator(f"Illegal `platform={str(platform)}`")
         kwargs['platform'] = platform_registry[platform]()
     else:
         kwargs['platform'] = configuration['platform']
@@ -1385,7 +1395,7 @@ def parse_kwargs(**kwargs):
         if not isinstance(language, str):
             raise ValueError("Argument `language` should be a `str`")
         if language not in configuration._accepted['language']:
-            raise InvalidOperator("Illegal `language=%s`" % str(language))
+            raise InvalidOperator(f"Illegal `language={str(language)}`")
         kwargs['language'] = language
     elif kwopenmp is not None:
         # Handle deprecated `openmp` kwarg for backward compatibility
@@ -1399,10 +1409,11 @@ def parse_kwargs(**kwargs):
         if not isinstance(compiler, str):
             raise ValueError("Argument `compiler` should be a `str`")
         if compiler not in configuration._accepted['compiler']:
-            raise InvalidOperator("Illegal `compiler=%s`" % str(compiler))
+            raise InvalidOperator(f"Illegal `compiler={str(compiler)}`")
         kwargs['compiler'] = compiler_registry[compiler](platform=kwargs['platform'],
                                                          language=kwargs['language'],
-                                                         mpi=configuration['mpi'])
+                                                         mpi=configuration['mpi'],
+                                                         name=compiler)
     elif any([platform, language]):
         kwargs['compiler'] =\
             configuration['compiler'].__new_with__(platform=kwargs['platform'],
@@ -1411,11 +1422,18 @@ def parse_kwargs(**kwargs):
     else:
         kwargs['compiler'] = configuration['compiler'].__new_with__()
 
+    # Make sure compiler and language are compatible
+    if compiler is not None and kwargs['compiler']._cpp and \
+            kwargs['language'] in ['C', 'openmp']:
+        kwargs['language'] = 'CXX' if kwargs['language'] == 'C' else 'CXXopenmp'
+    if 'CXX' in kwargs['language'] and not kwargs['compiler']._cpp:
+        kwargs['compiler'] = kwargs['compiler'].__new_with__(cpp=True)
+
     # `allocator`
     kwargs['allocator'] = default_allocator(
-        '%s.%s.%s' % (kwargs['compiler'].name,
-                      kwargs['language'],
-                      kwargs['platform'])
+        f"{kwargs['compiler'].__class__.__name__}"
+        f".{kwargs['language']}"
+        f".{kwargs['platform']}"
     )
 
     # Normalize `subs`, if any
diff --git a/devito/operator/registry.py b/devito/operator/registry.py
index 04c1000866..c8aac315b7 100644
--- a/devito/operator/registry.py
+++ b/devito/operator/registry.py
@@ -26,7 +26,8 @@ class OperatorRegistry(OrderedDict, metaclass=Singleton):
     """
 
     _modes = ('noop', 'advanced', 'advanced-fsg')
-    _languages = ('C', 'openmp', 'openacc', 'cuda', 'hip', 'sycl')
+    _languages = ('C', 'CXX', 'openmp', 'Copenmp', 'CXXopenmp',
+                  'openacc', 'cuda', 'hip', 'sycl')
     _accepted = _modes + tuple(product(_modes, _languages))
 
     def add(self, operator, platform, mode, language='C'):
diff --git a/devito/passes/clusters/aliases.py b/devito/passes/clusters/aliases.py
index 9c5ecde279..e828782812 100644
--- a/devito/passes/clusters/aliases.py
+++ b/devito/passes/clusters/aliases.py
@@ -109,11 +109,11 @@ class CireTransformer:
     def __init__(self, sregistry, options, platform):
         self.sregistry = sregistry
         self.platform = platform
-
         self.opt_minstorage = options['min-storage']
         self.opt_rotate = options['cire-rotate']
         self.opt_ftemps = options['cire-ftemps']
         self.opt_mingain = options['cire-mingain']
+        self.opt_min_dtype = options['scalar-min-type']
         self.opt_multisubdomain = True
 
     def _aliases_from_clusters(self, clusters, exclude, meta):
@@ -143,7 +143,7 @@ def _aliases_from_clusters(self, clusters, exclude, meta):
 
         # Schedule -> [Clusters]_k
         processed, subs = lower_schedule(schedule, meta, self.sregistry,
-                                         self.opt_ftemps)
+                                         self.opt_ftemps, self.opt_min_dtype)
 
         # [Clusters]_k -> [Clusters]_k (optimization)
         if self.opt_multisubdomain:
@@ -831,7 +831,7 @@ def optimize_schedule_rotations(schedule, sregistry):
     return schedule.rebuild(*processed, rmapper=rmapper)
 
 
-def lower_schedule(schedule, meta, sregistry, ftemps):
+def lower_schedule(schedule, meta, sregistry, ftemps, min_dtype):
     """
     Turn a Schedule into a sequence of Clusters.
     """
@@ -849,7 +849,6 @@ def lower_schedule(schedule, meta, sregistry, ftemps):
         # This prevents cases such as `floor(a*b)` with `a` and `b` floats
         # that would creat a temporary `int r = b` leading to erronous
         # numerical results
-        dtype = sympy_dtype(pivot, base=meta.dtype)
 
         if writeto:
             # The Dimensions defining the shape of Array
@@ -881,6 +880,7 @@ def lower_schedule(schedule, meta, sregistry, ftemps):
                     # E.g., `z` -- a non-shifted Dimension
                     indices.append(i.dim - i.lower)
 
+            dtype = sympy_dtype(pivot, base=meta.dtype)
             obj = make(name=name, dimensions=dimensions, halo=halo, dtype=dtype)
             expression = Eq(obj[indices], uxreplace(pivot, subs))
 
@@ -889,6 +889,7 @@ def lower_schedule(schedule, meta, sregistry, ftemps):
             # Degenerate case: scalar expression
             assert writeto.size == 0
 
+            dtype = sympy_dtype(pivot, base=meta.dtype, smin=min_dtype)
             obj = Temp(name=name, dtype=dtype)
             expression = Eq(obj, uxreplace(pivot, subs))
 
diff --git a/devito/passes/clusters/cse.py b/devito/passes/clusters/cse.py
index 30b4afa0af..f2677f9ca8 100644
--- a/devito/passes/clusters/cse.py
+++ b/devito/passes/clusters/cse.py
@@ -1,6 +1,7 @@
 from collections import defaultdict
 from functools import cached_property, singledispatch
 
+import numpy as np
 import sympy
 from sympy import Add, Function, Indexed, Mul, Pow
 try:
@@ -69,11 +70,15 @@ def cse(cluster, sregistry=None, options=None, **kwargs):
     """
     min_cost = options['cse-min-cost']
     mode = options['cse-algo']
+    try:
+        dtype = np.promote_types(options['scalar-min-type'], cluster.dtype).type
+    except TypeError:
+        dtype = cluster.dtype
 
     if cluster.is_fence:
         return cluster
 
-    make = lambda: CTemp(name=sregistry.make_name(), dtype=cluster.dtype)
+    make = lambda: CTemp(name=sregistry.make_name(), dtype=dtype)
 
     exprs = _cse(cluster, make, min_cost=min_cost, mode=mode)
 
diff --git a/devito/passes/clusters/derivatives.py b/devito/passes/clusters/derivatives.py
index f8f339aa1e..5af92a3208 100644
--- a/devito/passes/clusters/derivatives.py
+++ b/devito/passes/clusters/derivatives.py
@@ -1,6 +1,7 @@
 from functools import singledispatch
 
 from sympy import S
+import numpy as np
 
 from devito.finite_differences import IndexDerivative
 from devito.ir import Backward, Forward, Interval, IterationSpace, Queue
@@ -157,7 +158,7 @@ def _(expr, c, ispace, weights, reusables, mapper, **kwargs):
     # NOTE: created before recurring so that we ultimately get a sound ordering
     try:
         s = reusables.pop()
-        assert s.dtype is dtype
+        assert np.can_cast(s.dtype, dtype)
     except KeyError:
         name = sregistry.make_name(prefix='r')
         s = Symbol(name=name, dtype=dtype)
diff --git a/devito/passes/clusters/factorization.py b/devito/passes/clusters/factorization.py
index 8007d3fee2..45d140a253 100644
--- a/devito/passes/clusters/factorization.py
+++ b/devito/passes/clusters/factorization.py
@@ -195,7 +195,6 @@ def _collect_nested(expr, strategy):
     Recursion helper for `collect_nested`.
     """
     # Return semantic (rebuilt expression, factorization candidates)
-
     if expr.is_Number:
         return expr, {'coeffs': expr}
     elif q_routine(expr):
diff --git a/devito/passes/iet/__init__.py b/devito/passes/iet/__init__.py
index c09db00c9b..1cdb97c794 100644
--- a/devito/passes/iet/__init__.py
+++ b/devito/passes/iet/__init__.py
@@ -8,3 +8,4 @@
 from .instrument import *  # noqa
 from .languages import *  # noqa
 from .errors import *  # noqa
+from .dtypes import *  # noqa
diff --git a/devito/passes/iet/definitions.py b/devito/passes/iet/definitions.py
index 3532169754..3cc5446d57 100644
--- a/devito/passes/iet/definitions.py
+++ b/devito/passes/iet/definitions.py
@@ -16,7 +16,7 @@
 from devito.passes.iet.engine import iet_pass
 from devito.passes.iet.langbase import LangBB
 from devito.symbolics import (Byref, DefFunction, FieldFromPointer, IndexedPointer,
-                              SizeOf, VOID, Keyword, pow_to_mul)
+                              SizeOf, VOID, pow_to_mul)
 from devito.tools import as_mapper, as_list, as_tuple, filter_sorted, flatten
 from devito.types import (Array, ComponentAccess, CustomDimension, DeviceMap,
                           DeviceRM, Eq, Symbol)
@@ -70,7 +70,7 @@ def map(self, key, site, k, v):
 
 class DataManager:
 
-    lang = LangBB
+    langbb = LangBB
     """
     The language used to express data allocations, deletions, and host-device transfers.
     """
@@ -127,13 +127,13 @@ def _alloc_array_on_global_mem(self, site, obj, storage):
         name = self.sregistry.make_name(prefix='init_global')
         nbytes = SizeOf(obj._C_typedata)*obj.size
         body = [Definition(src),
-                self.lang['alloc-global-symbol'](obj.indexed, src.indexed, nbytes)]
+                self.langbb['alloc-global-symbol'](obj.indexed, src.indexed, nbytes)]
         efunc = make_callable(name, body)
         alloc = Call(name, efunc.parameters)
 
         storage.update(obj, site, allocs=alloc, efuncs=efunc)
 
-        return self.lang['header-memcpy']
+        return self.langbb['header-memcpy']
 
     def _alloc_scalar_on_low_lat_mem(self, site, expr, storage):
         """
@@ -150,9 +150,9 @@ def _alloc_host_array_on_high_bw_mem(self, site, obj, storage, *args):
         memptr = VOID(Byref(obj._C_symbol), '**')
         alignment = obj._data_alignment
         nbytes = SizeOf(obj._C_typedata)*obj.size
-        alloc = self.lang['host-alloc'](memptr, alignment, nbytes)
+        alloc = self.langbb['host-alloc'](memptr, alignment, nbytes)
 
-        free = self.lang['host-free'](obj._C_symbol)
+        free = self.langbb['host-free'](obj._C_symbol)
 
         storage.update(obj, site, allocs=(decl, alloc), frees=free)
 
@@ -172,7 +172,7 @@ def _alloc_mapped_array_on_high_bw_mem(self, site, obj, storage, *args):
         memptr = VOID(Byref(obj._C_symbol), '**')
         alignment = obj._data_alignment
         nbytes = SizeOf(obj._C_typedata)
-        allocs = [self.lang['host-alloc'](memptr, alignment, nbytes)]
+        allocs = [self.langbb['host-alloc'](memptr, alignment, nbytes)]
 
         nbytes_param = Symbol(name='nbytes', dtype=np.uint64, is_const=True)
         nbytes_arg = SizeOf(obj.indexed._C_typedata)*obj.size
@@ -180,7 +180,7 @@ def _alloc_mapped_array_on_high_bw_mem(self, site, obj, storage, *args):
         # Allocate the underlying host data
         ffp0 = FieldFromPointer(obj._C_field_data, obj._C_symbol)
         memptr = VOID(Byref(ffp0), '**')
-        allocs.append(self.lang['host-alloc-pin'](memptr, alignment, nbytes_param))
+        allocs.append(self.langbb['host-alloc-pin'](memptr, alignment, nbytes_param))
 
         # Initialize the Array struct
         ffp1 = FieldFromPointer(obj._C_field_nbytes, obj._C_symbol)
@@ -188,8 +188,8 @@ def _alloc_mapped_array_on_high_bw_mem(self, site, obj, storage, *args):
         ffp2 = FieldFromPointer(obj._C_field_size, obj._C_symbol)
         init1 = DummyExpr(ffp2, 0)
 
-        frees = [self.lang['host-free-pin'](ffp0),
-                 self.lang['host-free'](obj._C_symbol)]
+        frees = [self.langbb['host-free-pin'](ffp0),
+                 self.langbb['host-free'](obj._C_symbol)]
 
         # Allocate the underlying device data, if required by the backend
         alloc, free = self._make_dmap_allocfree(obj, nbytes_param)
@@ -226,7 +226,7 @@ def _alloc_bundle_struct_on_high_bw_mem(self, site, obj, storage):
         memptr = VOID(Byref(obj._C_symbol), '**')
         alignment = obj._data_alignment
         nbytes = SizeOf(obj._C_typedata)
-        alloc = self.lang['host-alloc'](memptr, alignment, nbytes)
+        alloc = self.langbb['host-alloc'](memptr, alignment, nbytes)
 
         nbytes_param = Symbol(name='nbytes', dtype=np.uint64, is_const=True)
         nbytes_arg = SizeOf(obj.indexed._C_typedata)*obj.size
@@ -237,7 +237,7 @@ def _alloc_bundle_struct_on_high_bw_mem(self, site, obj, storage):
         ffp2 = FieldFromPointer(obj._C_field_size, obj._C_symbol)
         init1 = DummyExpr(ffp2, 0)
 
-        free = self.lang['host-free'](obj._C_symbol)
+        free = self.langbb['host-free'](obj._C_symbol)
 
         ret = Return(obj._C_symbol)
 
@@ -276,18 +276,18 @@ def _alloc_pointed_array_on_high_bw_mem(self, site, obj, storage):
 
         memptr = VOID(Byref(obj._C_symbol), '**')
         alignment = obj._data_alignment
-        nbytes = SizeOf(Keyword('%s*' % obj._C_typedata))*obj.dim.symbolic_size
-        alloc0 = self.lang['host-alloc'](memptr, alignment, nbytes)
+        nbytes = SizeOf(obj._C_typedata, stars='*')*obj.dim.symbolic_size
+        alloc0 = self.langbb['host-alloc'](memptr, alignment, nbytes)
 
-        free0 = self.lang['host-free'](obj._C_symbol)
+        free0 = self.langbb['host-free'](obj._C_symbol)
 
         # The pointee Array
         pobj = IndexedPointer(obj._C_symbol, obj.dim)
         memptr = VOID(Byref(pobj), '**')
         nbytes = SizeOf(obj._C_typedata)*obj.array.size
-        alloc1 = self.lang['host-alloc'](memptr, alignment, nbytes)
+        alloc1 = self.langbb['host-alloc'](memptr, alignment, nbytes)
 
-        free1 = self.lang['host-free'](pobj)
+        free1 = self.langbb['host-free'](pobj)
 
         # Dump
         if obj.dim is self.sregistry.threadid:
@@ -322,15 +322,15 @@ def _inject_definitions(self, iet, storage):
             allocs = as_list(cbody.allocs) + flatten(v.allocs)
             stacks = as_list(cbody.stacks) + flatten(v.stacks)
             for tid, body in as_mapper(v.pallocs, itemgetter(0), itemgetter(1)).items():
-                header = self.lang.Region._make_header(tid.symbolic_size)
-                init = self.lang['thread-num'](retobj=tid)
+                header = self.langbb.Region._make_header(tid.symbolic_size)
+                init = self.langbb['thread-num'](retobj=tid)
                 allocs.append(Block(header=header, body=[init] + body))
 
             # frees/pfrees
             frees = []
             for tid, body in as_mapper(v.pfrees, itemgetter(0), itemgetter(1)).items():
-                header = self.lang.Region._make_header(tid.symbolic_size)
-                init = self.lang['thread-num'](retobj=tid)
+                header = self.langbb.Region._make_header(tid.symbolic_size)
+                init = self.langbb['thread-num'](retobj=tid)
                 frees.append(Block(header=header, body=[init] + body))
             frees.extend(as_list(cbody.frees) + flatten(v.frees))
 
@@ -455,7 +455,7 @@ def place_casts(self, iet, **kwargs):
         bases = [i for i in bases if i.name != i.function._C_name]
 
         # Create and attach the type casts
-        casts = tuple(self.lang.PointerCast(i.function, obj=i) for i in bases
+        casts = tuple(self.langbb.PointerCast(i.function, obj=i) for i in bases
                       if i not in defines)
         if casts:
             iet = iet._rebuild(body=iet.body._rebuild(casts=casts + iet.body.casts))
@@ -483,9 +483,9 @@ def _alloc_local_array_on_high_bw_mem(self, site, obj, storage):
         """
         Allocate a local Array in the device high bandwidth memory.
         """
-        deviceid = DefFunction(self.lang['device-get'].name)
-        doalloc = self.lang['device-alloc']
-        dofree = self.lang['device-free']
+        deviceid = DefFunction(self.langbb['device-get'].name)
+        doalloc = self.langbb['device-alloc']
+        dofree = self.langbb['device-free']
 
         nbytes = SizeOf(obj._C_typedata)*obj.size
         init = doalloc(nbytes, deviceid, retobj=obj)
@@ -503,8 +503,8 @@ def _map_array_on_high_bw_mem(self, site, obj, storage):
         if not obj._mem_mapped:
             return
 
-        mmap = self.lang._map_alloc(obj)
-        unmap = self.lang._map_delete(obj)
+        mmap = self.langbb._map_alloc(obj)
+        unmap = self.langbb._map_delete(obj)
 
         storage.update(obj, site, maps=mmap, unmaps=unmap)
 
@@ -521,22 +521,22 @@ def _map_function_on_high_bw_mem(self, site, obj, storage, devicerm, read_only=F
         """
         if read_only is False:
             if is_gpu_create(obj, self.gpu_create):
-                mmap = self.lang._map_alloc(obj)
+                mmap = self.langbb._map_alloc(obj)
 
                 efuncs, init = make_zero_init(obj, self.rcompile, self.sregistry)
 
                 mmap = (mmap, init)
             else:
-                mmap = self.lang._map_to(obj)
+                mmap = self.langbb._map_to(obj)
                 efuncs = ()
 
             # Copy back to host memory, release device memory
-            unmap = (self.lang._map_update(obj),
-                     self.lang._map_release(obj, devicerm=devicerm))
+            unmap = (self.langbb._map_update(obj),
+                     self.langbb._map_release(obj, devicerm=devicerm))
         else:
-            mmap = self.lang._map_to(obj)
+            mmap = self.langbb._map_to(obj)
             efuncs = ()
-            unmap = self.lang._map_delete(obj, devicerm=devicerm)
+            unmap = self.langbb._map_delete(obj, devicerm=devicerm)
 
         storage.update(obj, site, maps=mmap, unmaps=unmap, efuncs=efuncs)
 
@@ -587,7 +587,7 @@ def place_devptr(self, iet, **kwargs):
         dmaps = [i for i in FindSymbols('basics').visit(iet)
                  if isinstance(i, DeviceMap) and i not in defines]
 
-        maps = [self.lang.PointerCast(i.function, obj=i) for i in dmaps]
+        maps = [self.langbb.PointerCast(i.function, obj=i) for i in dmaps]
         body = iet.body._rebuild(maps=iet.body.maps + tuple(maps))
         iet = iet._rebuild(body=body)
 
diff --git a/devito/passes/iet/dtypes.py b/devito/passes/iet/dtypes.py
new file mode 100644
index 0000000000..28d30df13b
--- /dev/null
+++ b/devito/passes/iet/dtypes.py
@@ -0,0 +1,55 @@
+import numpy as np
+
+from devito.arch.compiler import Compiler
+from devito.ir import Callable, SymbolRegistry
+from devito.ir.iet.utils import has_dtype
+from devito.passes.iet.engine import iet_pass
+from devito.passes.iet.langbase import LangBB
+from devito.tools import as_tuple
+
+__all__ = ['lower_dtypes']
+
+
+@iet_pass
+def _complex_includes(iet: Callable, langbb: type[LangBB], compiler: Compiler,
+                      sregistry: SymbolRegistry) -> tuple[Callable, dict]:
+    """
+    Includes complex arithmetic headers for the given language, if needed.
+    """
+    # Check if there are complex numbers that always take dtype precedence
+    if not has_dtype(iet, np.complexfloating):
+        return iet, {}
+
+    metadata = {}
+    lib = as_tuple(langbb['includes-complex'])
+
+    if langbb.get('complex-namespace') is not None:
+        metadata['namespaces'] = langbb['complex-namespace']
+
+    # Some languges such as c++11 need some extra arithmetic definitions
+    if langbb.get('def-complex'):
+        dest = compiler.get_jit_dir()
+        hfile = dest.joinpath('complex_arith.h')
+        with open(str(hfile), 'w') as ff:
+            ff.write(str(langbb['def-complex']))
+        lib += (str(hfile),)
+
+    metadata['includes'] = lib
+
+    return iet, metadata
+
+
+dtype_passes = [_complex_includes]
+
+
+def lower_dtypes(graph: Callable,
+                 langbb: type[LangBB] = None,
+                 compiler: Compiler = None,
+                 sregistry: SymbolRegistry = None, **kwargs) -> tuple[Callable, dict]:
+    """
+    Lowers float16 scalar types to pointers since we can't directly pass their
+    value. Also includes headers for complex arithmetic if needed.
+    """
+
+    for dtype_pass in dtype_passes:
+        dtype_pass(graph, langbb=langbb, compiler=compiler, sregistry=sregistry)
diff --git a/devito/passes/iet/errors.py b/devito/passes/iet/errors.py
index fb89934e46..8082ae53cf 100644
--- a/devito/passes/iet/errors.py
+++ b/devito/passes/iet/errors.py
@@ -6,7 +6,7 @@
                            List, Break, Return, FindNodes, FindSymbols, Transformer,
                            make_callable)
 from devito.passes.iet.engine import iet_pass
-from devito.symbolics import CondEq, DefFunction
+from devito.symbolics import CondEq, MathFunction
 from devito.tools import dtype_to_ctype
 from devito.types import Eq, Inc, LocalObject, Symbol
 
@@ -58,7 +58,7 @@ def _check_stability(iet, wmovs=(), rcompile=None, sregistry=None):
         irs, byproduct = rcompile(eqns)
 
         name = sregistry.make_name(prefix='is_finite')
-        retval = Return(DefFunction('isfinite', accumulator))
+        retval = Return(MathFunction('isfinite', accumulator))
         body = irs.iet.body.body + (retval,)
         efunc = make_callable(name, body, retval='int')
 
diff --git a/devito/passes/iet/instrument.py b/devito/passes/iet/instrument.py
index 7462c8f07c..5ad934d13f 100644
--- a/devito/passes/iet/instrument.py
+++ b/devito/passes/iet/instrument.py
@@ -121,12 +121,12 @@ def instrument_sections(iet, **kwargs):
 
 
 @iet_pass
-def sync_sections(iet, lang=None, profiler=None, **kwargs):
+def sync_sections(iet, langbb=None, profiler=None, **kwargs):
     """
     Wrap sections within global barriers if deemed necessary by the profiler.
     """
     try:
-        sync = lang['map-wait']
+        sync = langbb['map-wait']
     except (KeyError, NotImplementedError):
         return iet, {}
 
@@ -137,7 +137,7 @@ def sync_sections(iet, lang=None, profiler=None, **kwargs):
     for tl in FindNodes(TimedList).visit(iet):
         symbols = FindSymbols().visit(tl)
 
-        queues = [i for i in symbols if isinstance(i, lang.AsyncQueue)]
+        queues = [i for i in symbols if isinstance(i, langbb.AsyncQueue)]
         unnecessary = any(FindNodes(BusyWait).visit(tl))
         if queues and not unnecessary:
             waits = tuple(sync(i) for i in queues)
diff --git a/devito/passes/iet/langbase.py b/devito/passes/iet/langbase.py
index d27674c419..d2542309e2 100644
--- a/devito/passes/iet/langbase.py
+++ b/devito/passes/iet/langbase.py
@@ -31,6 +31,9 @@ def __getitem__(self, k):
             raise NotImplementedError("Missing required mapping for `%s`" % k)
         return self.mapper[k]
 
+    def get(self, k, v=None):
+        return self.mapper.get(k, v)
+
 
 class LangBB(metaclass=LangMeta):
 
@@ -151,7 +154,7 @@ class LangTransformer(ABC):
     an IET for a certain target language (e.g., C, C+OpenMP).
     """
 
-    lang = LangBB
+    langbb = LangBB
     """
     The constructs of the target language. To be specialized by a subclass.
     """
@@ -202,19 +205,19 @@ def initialize(self, iet, options=None):
 
     @property
     def Region(self):
-        return self.lang.Region
+        return self.langbb.Region
 
     @property
     def HostIteration(self):
-        return self.lang.HostIteration
+        return self.langbb.HostIteration
 
     @property
     def DeviceIteration(self):
-        return self.lang.DeviceIteration
+        return self.langbb.DeviceIteration
 
     @property
     def Prodder(self):
-        return self.lang.Prodder
+        return self.langbb.Prodder
 
 
 class ShmTransformer(LangTransformer):
@@ -433,11 +436,11 @@ def _(iet):
                     except AttributeError:
                         pass
 
-            devicetype = as_list(self.lang[self.platform])
+            devicetype = as_list(self.langbb[self.platform])
             deviceid = self.deviceid
 
             try:
-                lang_init = [self.lang['init'](devicetype)]
+                lang_init = [self.langbb['init'](devicetype)]
             except TypeError:
                 # Not all target languages need to be explicitly initialized
                 lang_init = []
@@ -447,10 +450,10 @@ def _(iet):
                 rank_decl = DummyExpr(rank, 0)
                 rank_init = Call('MPI_Comm_rank', [objcomm, Byref(rank)])
 
-                ngpus, call_ngpus = self.lang._get_num_devices(self.platform)
+                ngpus, call_ngpus = self.langbb._get_num_devices(self.platform)
 
-                osdd_then = self.lang['set-device']([deviceid] + devicetype)
-                osdd_else = self.lang['set-device']([rank % ngpus] + devicetype)
+                osdd_then = self.langbb['set-device']([deviceid] + devicetype)
+                osdd_else = self.langbb['set-device']([rank % ngpus] + devicetype)
 
                 body = lang_init + [Conditional(
                     CondNe(deviceid, -1),
@@ -458,16 +461,16 @@ def _(iet):
                     List(body=[rank_decl, rank_init, call_ngpus, osdd_else]),
                 )]
 
-                header = c.Comment('Begin of %s+MPI setup' % self.lang['name'])
-                footer = c.Comment('End of %s+MPI setup' % self.lang['name'])
+                header = c.Comment('Begin of %s+MPI setup' % self.langbb['name'])
+                footer = c.Comment('End of %s+MPI setup' % self.langbb['name'])
             else:
                 body = lang_init + [Conditional(
                     CondNe(deviceid, -1),
-                    self.lang['set-device']([deviceid] + devicetype)
+                    self.langbb['set-device']([deviceid] + devicetype)
                 )]
 
-                header = c.Comment('Begin of %s setup' % self.lang['name'])
-                footer = c.Comment('End of %s setup' % self.lang['name'])
+                header = c.Comment('Begin of %s setup' % self.langbb['name'])
+                footer = c.Comment('End of %s setup' % self.langbb['name'])
 
             init = List(header=header, body=body, footer=footer)
             iet = iet._rebuild(body=iet.body._rebuild(init=init))
@@ -476,12 +479,12 @@ def _(iet):
 
         @_initialize.register(AsyncCallable)
         def _(iet):
-            devicetype = as_list(self.lang[self.platform])
+            devicetype = as_list(self.langbb[self.platform])
             deviceid = self.deviceid
 
             init = Conditional(
                 CondNe(deviceid, -1),
-                self.lang['set-device']([deviceid] + devicetype)
+                self.langbb['set-device']([deviceid] + devicetype)
             )
             iet = iet._rebuild(body=iet.body._rebuild(init=init))
 
diff --git a/devito/passes/iet/languages/C.py b/devito/passes/iet/languages/C.py
index 4b3358798d..bfb0935a20 100644
--- a/devito/passes/iet/languages/C.py
+++ b/devito/passes/iet/languages/C.py
@@ -1,7 +1,11 @@
-from devito.ir import Call
+import numpy as np
+from sympy.printing.c import C99CodePrinter
+
+from devito.ir import Call, BasePrinter
 from devito.passes.iet.definitions import DataManager
 from devito.passes.iet.orchestration import Orchestrator
 from devito.passes.iet.langbase import LangBB
+from devito.symbolics.extended_dtypes import c_complex, c_double_complex
 
 __all__ = ['CBB', 'CDataManager', 'COrchestrator']
 
@@ -9,6 +13,9 @@
 class CBB(LangBB):
 
     mapper = {
+        # Complex
+        'includes-complex': 'complex.h',
+        # Allocs
         'header-memcpy': 'string.h',
         'host-alloc': lambda i, j, k:
             Call('posix_memalign', (i, j, k)),
@@ -19,13 +26,32 @@ class CBB(LangBB):
         'host-free-pin': lambda i:
             Call('free', (i,)),
         'alloc-global-symbol': lambda i, j, k:
-            Call('memcpy', (i, j, k))
+            Call('memcpy', (i, j, k)),
     }
 
 
 class CDataManager(DataManager):
-    lang = CBB
+    langbb = CBB
 
 
 class COrchestrator(Orchestrator):
-    lang = CBB
+    langbb = CBB
+
+
+class CPrinter(BasePrinter, C99CodePrinter):
+
+    _default_settings = {**BasePrinter._default_settings,
+                         **C99CodePrinter._default_settings}
+    _func_literals = {np.float32: 'f', np.complex64: 'f'}
+    _func_prefix = {np.float32: 'f', np.float64: 'f',
+                    np.complex64: 'c', np.complex128: 'c'}
+    _includes = ['stdlib.h', 'math.h', 'sys/time.h']
+
+    # These cannot go through _print_xxx because they are classes not
+    # instances
+    type_mappings = {**C99CodePrinter.type_mappings,
+                     c_complex: 'float _Complex',
+                     c_double_complex: 'double _Complex'}
+
+    def _print_ImaginaryUnit(self, expr):
+        return '_Complex_I'
diff --git a/devito/passes/iet/languages/CXX.py b/devito/passes/iet/languages/CXX.py
new file mode 100644
index 0000000000..82e099e88c
--- /dev/null
+++ b/devito/passes/iet/languages/CXX.py
@@ -0,0 +1,114 @@
+import numpy as np
+from sympy.printing.cxx import CXX11CodePrinter
+
+from devito.ir import Call, UsingNamespace, BasePrinter
+from devito.passes.iet.langbase import LangBB
+from devito.symbolics.extended_dtypes import c_complex, c_double_complex
+
+__all__ = ['CXXBB']
+
+
+def std_arith(prefix=None):
+    if prefix:
+        # Method definition prefix, e.g. "__host__"
+        # Make sure there is a space between the prefix and the method name
+        prefix = prefix if prefix.endswith(" ") else f"{prefix} "
+    else:
+        prefix = ""
+    return f"""
+#include <complex>
+
+template<typename _Tp, typename _Ti>
+{prefix}std::complex<_Tp> operator * (const _Ti & a, const std::complex<_Tp> & b){{
+  return std::complex<_Tp>(b.real() * a, b.imag() * a);
+}}
+
+template<typename _Tp, typename _Ti>
+{prefix}std::complex<_Tp> operator * (const std::complex<_Tp> & b, const _Ti & a){{
+  return std::complex<_Tp>(b.real() * a, b.imag() * a);
+}}
+
+template<typename _Tp, typename _Ti>
+{prefix}std::complex<_Tp> operator / (const _Ti & a, const std::complex<_Tp> & b){{
+  _Tp denom = b.real() * b.real () + b.imag() * b.imag();
+  return std::complex<_Tp>(b.real() * a / denom, - b.imag() * a / denom);
+}}
+
+template<typename _Tp, typename _Ti>
+{prefix}std::complex<_Tp> operator / (const std::complex<_Tp> & b, const _Ti & a){{
+  return std::complex<_Tp>(b.real() / a, b.imag() / a);
+}}
+
+template<typename _Tp, typename _Ti>
+{prefix}std::complex<_Tp> operator + (const _Ti & a, const std::complex<_Tp> & b){{
+  return std::complex<_Tp>(b.real() + a, b.imag());
+}}
+
+template<typename _Tp, typename _Ti>
+{prefix}std::complex<_Tp> operator + (const std::complex<_Tp> & b, const _Ti & a){{
+  return std::complex<_Tp>(b.real() + a, b.imag());
+}}
+
+template<typename _Tp, typename _Ti>
+{prefix}std::complex<_Tp> operator - (const _Ti & a, const std::complex<_Tp> & b){{
+  return std::complex<_Tp>(a = b.real(), b.imag());
+}}
+
+template<typename _Tp, typename _Ti>
+{prefix}std::complex<_Tp> operator - (const std::complex<_Tp> & b, const _Ti & a){{
+  return std::complex<_Tp>(b.real() - a, b.imag());
+}}
+
+"""
+
+
+class CXXBB(LangBB):
+
+    mapper = {
+        # Complex
+        'includes-complex': 'complex',
+        'complex-namespace': [UsingNamespace('std::complex_literals')],
+        'def-complex': std_arith(),
+        # Allocs
+        'header-memcpy': 'string.h',
+        'header-math': 'algorithm',
+        'host-alloc': lambda i, j, k:
+            Call('posix_memalign', (i, j, k)),
+        'host-alloc-pin': lambda i, j, k:
+            Call('posix_memalign', (i, j, k)),
+        'host-free': lambda i:
+            Call('free', (i,)),
+        'host-free-pin': lambda i:
+            Call('free', (i,)),
+        'alloc-global-symbol': lambda i, j, k:
+            Call('memcpy', (i, j, k)),
+    }
+
+
+class CXXPrinter(BasePrinter, CXX11CodePrinter):
+
+    _default_settings = {**BasePrinter._default_settings,
+                         **CXX11CodePrinter._default_settings}
+    _ns = "std::"
+    _func_literals = {}
+    _func_prefix = {np.float32: 'f', np.float64: 'f'}
+    _restrict_keyword = '__restrict'
+    _includes = ['stdlib.h', 'cmath', 'sys/time.h']
+
+    # These cannot go through _print_xxx because they are classes not
+    # instances
+    type_mappings = {**CXX11CodePrinter.type_mappings,
+                     c_complex: 'std::complex<float>',
+                     c_double_complex: 'std::complex<double>'}
+
+    def _print_ImaginaryUnit(self, expr):
+        return f'1i{self.prec_literal(expr).lower()}'
+
+    def _print_Cast(self, expr):
+        # The CXX recommended way to cast is to use static_cast
+        tstr = self._print(expr._C_ctype)
+        if 'void' in tstr:
+            return super()._print_Cast(expr)
+        caster = 'reinterpret_cast' if expr.reinterpret else 'static_cast'
+        cast = f'{caster}<{tstr}{self._print(expr.stars)}>'
+        return self._print_UnaryOp(expr, op=cast, parenthesize=True)
diff --git a/devito/passes/iet/languages/openacc.py b/devito/passes/iet/languages/openacc.py
index bcd2c8d006..bacd2a5f66 100644
--- a/devito/passes/iet/languages/openacc.py
+++ b/devito/passes/iet/languages/openacc.py
@@ -9,9 +9,9 @@
 from devito.passes.iet.orchestration import Orchestrator
 from devito.passes.iet.parpragma import (PragmaDeviceAwareTransformer, PragmaLangBB,
                                          PragmaIteration, PragmaTransfer)
-from devito.passes.iet.languages.C import CBB
+from devito.passes.iet.languages.CXX import CXXBB, CXXPrinter
 from devito.passes.iet.languages.openmp import OmpRegion, OmpIteration
-from devito.symbolics import FieldFromPointer, Macro, cast_mapper
+from devito.symbolics import FieldFromPointer, Macro, cast
 from devito.tools import filter_ordered, UnboundTuple
 from devito.types import Symbol
 
@@ -122,7 +122,8 @@ class AccBB(PragmaLangBB):
         'device-free': lambda i, *a:
             Call('acc_free', (i,))
     }
-    mapper.update(CBB.mapper)
+
+    mapper.update(CXXBB.mapper)
 
     Region = OmpRegion
     HostIteration = OmpIteration  # Host parallelism still goes via OpenMP
@@ -161,7 +162,7 @@ def _map_update_device_async(cls, f, imask=None, qid=None):
 
 class DeviceAccizer(PragmaDeviceAwareTransformer):
 
-    lang = AccBB
+    langbb = AccBB
 
     def _make_partree(self, candidates, nthreads=None):
         assert candidates
@@ -186,7 +187,7 @@ def _make_partree(self, candidates, nthreads=None):
 
 class DeviceAccDataManager(DeviceAwareDataManager):
 
-    lang = AccBB
+    langbb = AccBB
 
     @iet_pass
     def place_devptr(self, iet, **kwargs):
@@ -234,17 +235,17 @@ def place_devptr(self, iet, **kwargs):
             init = DummyExpr(tdp, 0, init=True)
 
             dpf = List(body=[
-                self.lang.mapper['map-serial-present'](hp, tdp),
-                Block(body=DummyExpr(tdp, cast_mapper[tdp.dtype](hp)))
+                self.langbb.mapper['map-serial-present'](hp, tdp),
+                Block(body=DummyExpr(tdp, cast(tdp.dtype)(hp, reinterpret=True)))
             ])
 
             ffp = FieldFromPointer(f._C_field_dmap, f._C_symbol)
-            ctdp = cast_mapper[(hp.dtype, '*')](tdp)
-            cast = DummyExpr(ffp, ctdp)
+            ctdp = cast(hp.dtype, '*')(tdp, reinterpret=True)
+            castf = DummyExpr(ffp, ctdp)
 
             ret = Return(ctdp)
 
-            body = List(body=[init, dpf, cast, ret])
+            body = List(body=[init, dpf, castf, ret])
 
             name = self.sregistry.make_name(prefix='map_device_ptr')
             efuncs.append(make_callable(name, body, retval=hp))
@@ -261,4 +262,8 @@ def place_devptr(self, iet, **kwargs):
 
 
 class AccOrchestrator(Orchestrator):
-    lang = AccBB
+    langbb = AccBB
+
+
+class AccPrinter(CXXPrinter):
+    pass
diff --git a/devito/passes/iet/languages/openmp.py b/devito/passes/iet/languages/openmp.py
index ad12879b25..94b68acfb6 100644
--- a/devito/passes/iet/languages/openmp.py
+++ b/devito/passes/iet/languages/openmp.py
@@ -87,7 +87,7 @@ class ThreadedProdder(Conditional, Prodder):
 
     def __init__(self, prodder, arguments=None):
         # Atomic-ize any single-thread Prodders in the parallel tree
-        condition = CondEq(DefFunction(Ompizer.lang['thread-num']().name), 0)
+        condition = CondEq(DefFunction(Ompizer.langbb['thread-num']().name), 0)
 
         # Prod within a while loop until all communications have completed
         # In other words, the thread delegated to prodding is entrapped for as long
@@ -210,12 +210,12 @@ def _map_delete(cls, f, imask=None, devicerm=None):
 
 
 class SimdOmpizer(PragmaSimdTransformer):
-    lang = OmpBB
+    langbb = OmpBB
 
 
 class Ompizer(PragmaShmTransformer):
 
-    lang = OmpBB
+    langbb = OmpBB
 
     @classmethod
     def _support_array_reduction(cls, compiler):
@@ -228,20 +228,20 @@ def _support_array_reduction(cls, compiler):
 
 
 class DeviceOmpizer(PragmaDeviceAwareTransformer):
-    lang = DeviceOmpBB
+    langbb = DeviceOmpBB
 
 
 class OmpDataManager(DataManager):
-    lang = OmpBB
+    langbb = OmpBB
 
 
 class DeviceOmpDataManager(DeviceAwareDataManager):
-    lang = DeviceOmpBB
+    langbb = DeviceOmpBB
 
 
 class OmpOrchestrator(Orchestrator):
-    lang = OmpBB
+    langbb = OmpBB
 
 
 class DeviceOmpOrchestrator(Orchestrator):
-    lang = DeviceOmpBB
+    langbb = DeviceOmpBB
diff --git a/devito/passes/iet/languages/targets.py b/devito/passes/iet/languages/targets.py
index 4ac8d94398..3ca64e1c10 100644
--- a/devito/passes/iet/languages/targets.py
+++ b/devito/passes/iet/languages/targets.py
@@ -1,47 +1,81 @@
-from devito.passes.iet.languages.C import CDataManager, COrchestrator
+from devito.passes.iet.languages.C import CDataManager, COrchestrator, CPrinter
+from devito.passes.iet.languages.CXX import CXXPrinter
 from devito.passes.iet.languages.openmp import (SimdOmpizer, Ompizer, DeviceOmpizer,
                                                 OmpDataManager, DeviceOmpDataManager,
                                                 OmpOrchestrator, DeviceOmpOrchestrator)
 from devito.passes.iet.languages.openacc import (DeviceAccizer, DeviceAccDataManager,
-                                                 AccOrchestrator)
+                                                 AccOrchestrator, AccPrinter)
 from devito.passes.iet.instrument import instrument
 
-__all__ = ['CTarget', 'OmpTarget', 'DeviceOmpTarget', 'DeviceAccTarget']
+__all__ = ['CTarget', 'OmpTarget', 'COmpTarget', 'DeviceOmpTarget', 'DeviceAccTarget',
+           'CXXTarget', 'CXXOmpTarget', 'DeviceCXXOmpTarget']
 
 
 class Target:
     Parizer = None
     DataManager = None
     Orchestrator = None
+    Printer = None
 
     @classmethod
-    def lang(cls):
-        return cls.Parizer.lang
+    def langbb(cls):
+        return cls.Parizer.langbb
 
     @classmethod
     def instrument(cls, *args, **kwargs):
-        instrument(*args, lang=cls.lang(), **kwargs)
+        instrument(*args, **kwargs)
 
 
 class CTarget(Target):
     Parizer = SimdOmpizer
     DataManager = CDataManager
     Orchestrator = COrchestrator
+    Printer = CPrinter
 
 
-class OmpTarget(Target):
+class CXXTarget(Target):
+    Parizer = SimdOmpizer
+    DataManager = CDataManager
+    Orchestrator = COrchestrator
+    Printer = CXXPrinter
+
+
+class COmpTarget(Target):
     Parizer = Ompizer
     DataManager = OmpDataManager
     Orchestrator = OmpOrchestrator
+    Printer = CPrinter
+
+
+OmpTarget = COmpTarget
+
+
+class CXXOmpTarget(Target):
+    Parizer = Ompizer
+    DataManager = OmpDataManager
+    Orchestrator = OmpOrchestrator
+    Printer = CXXPrinter
+
+
+class DeviceCOmpTarget(Target):
+    Parizer = DeviceOmpizer
+    DataManager = DeviceOmpDataManager
+    Orchestrator = DeviceOmpOrchestrator
+    Printer = CPrinter
+
+
+DeviceOmpTarget = DeviceCOmpTarget
 
 
-class DeviceOmpTarget(Target):
+class DeviceCXXOmpTarget(Target):
     Parizer = DeviceOmpizer
     DataManager = DeviceOmpDataManager
     Orchestrator = DeviceOmpOrchestrator
+    Printer = CXXPrinter
 
 
 class DeviceAccTarget(Target):
     Parizer = DeviceAccizer
     DataManager = DeviceAccDataManager
     Orchestrator = AccOrchestrator
+    Printer = AccPrinter
diff --git a/devito/passes/iet/misc.py b/devito/passes/iet/misc.py
index 34f4f367c3..e404a8e373 100644
--- a/devito/passes/iet/misc.py
+++ b/devito/passes/iet/misc.py
@@ -6,15 +6,16 @@
 
 from devito.finite_differences import Max, Min
 from devito.finite_differences.differentiable import SafeInv
+from devito.logger import warning
 from devito.ir import (Any, Forward, DummyExpr, Iteration, List, Prodder,
                        FindApplications, FindNodes, FindSymbols, Transformer,
                        Uxreplace, filter_iterations, retrieve_iteration_tree,
                        pull_dims)
 from devito.passes.iet.engine import iet_pass
+from devito.passes.iet.languages.C import CPrinter
 from devito.ir.iet.efunc import DeviceFunction, EntryFunction
-from devito.symbolics import (ValueLimit, evalrel, has_integer_args, limits_mapper,
-                              ccode)
-from devito.tools import Bunch, as_mapper, filter_ordered, split
+from devito.symbolics import (ValueLimit, evalrel, has_integer_args, limits_mapper, Cast)
+from devito.tools import Bunch, as_mapper, filter_ordered, split, as_tuple
 from devito.types import FIndexed
 
 __all__ = ['avoid_denormals', 'hoist_prodders', 'relax_incr_dimensions',
@@ -144,16 +145,19 @@ def generate_macros(graph, **kwargs):
 
 
 @iet_pass
-def _generate_macros(iet, tracker=None, **kwargs):
+def _generate_macros(iet, tracker=None, langbb=None, printer=CPrinter, **kwargs):
     # Derive the Macros necessary for the FIndexeds
     iet = _generate_macros_findexeds(iet, tracker=tracker, **kwargs)
 
     # NOTE: sorting is necessary to ensure deterministic code generation
     headers = [i.header for i in tracker.values()]
-    headers = sorted((ccode(define), ccode(expr)) for define, expr in headers)
+    headers = sorted((printer()._print(define), printer()._print(expr))
+                     for define, expr in headers)
 
     # Generate Macros from higher-level SymPy objects
-    headers.extend(sorted(_generate_macros_math(iet), key=str))
+    mheaders, includes = _generate_macros_math(iet, langbb=langbb)
+    includes = sorted(includes, key=str)
+    headers.extend(sorted(mheaders, key=str))
 
     # Remove redundancies while preserving the order
     headers = filter_ordered(headers)
@@ -161,11 +165,10 @@ def _generate_macros(iet, tracker=None, **kwargs):
     # Some special Symbols may represent Macros defined in standard libraries,
     # so we need to include the respective includes
     limits = FindApplications(ValueLimit).visit(iet)
-    includes = set()
     if limits & (set(limits_mapper[np.int32]) | set(limits_mapper[np.int64])):
-        includes.add('limits.h')
+        includes.append('limits.h')
     elif limits & (set(limits_mapper[np.float32]) | set(limits_mapper[np.float64])):
-        includes.add('float.h')
+        includes.append('float.h')
 
     return iet, {'headers': headers, 'includes': includes}
 
@@ -196,42 +199,50 @@ def _generate_macros_findexeds(iet, sregistry=None, tracker=None, **kwargs):
     return iet
 
 
-def _generate_macros_math(iet):
+def _generate_macros_math(iet, langbb=None):
     headers = []
+    includes = []
     for i in FindApplications().visit(iet):
-        headers.extend(_lower_macro_math(i))
+        header, include = _lower_macro_math(i, langbb)
+        headers.extend(header)
+        includes.extend(include)
 
-    return headers
+    return headers, set(includes) - {None}
 
 
 @singledispatch
-def _lower_macro_math(expr):
-    return ()
+def _lower_macro_math(expr, langbb):
+    return (), {}
 
 
 @_lower_macro_math.register(Min)
 @_lower_macro_math.register(sympy.Min)
-def _(expr):
-    if has_integer_args(*expr.args) and len(expr.args) == 2:
-        return (('MIN(a,b)', ('(((a) < (b)) ? (a) : (b))')),)
+def _(expr, langbb):
+    if has_integer_args(*expr.args):
+        return (('MIN(a,b)', ('(((a) < (b)) ? (a) : (b))')),), {}
     else:
-        return ()
+        return (), as_tuple(langbb.get('header-math'))
 
 
 @_lower_macro_math.register(Max)
 @_lower_macro_math.register(sympy.Max)
-def _(expr):
-    if has_integer_args(*expr.args) and len(expr.args) == 2:
-        return (('MAX(a,b)', ('(((a) > (b)) ? (a) : (b))')),)
+def _(expr, langbb):
+    if has_integer_args(*expr.args):
+        return (('MAX(a,b)', ('(((a) > (b)) ? (a) : (b))')),), {}
     else:
-        return ()
+        return (), as_tuple(langbb.get('header-math'))
 
 
 @_lower_macro_math.register(SafeInv)
-def _(expr):
-    eps = np.finfo(np.float32).resolution**2
+def _(expr, langbb):
+    try:
+        eps = np.finfo(expr.base.dtype).resolution**2
+    except ValueError:
+        warning(f"dtype not recognized in SafeInv for {expr.base}, assuming float32")
+        eps = np.finfo(np.float32).resolution**2
+    b = Cast('b', dtype=np.float32)
     return (('SAFEINV(a, b)',
-             f'(((a) < {eps} || (b) < {eps}) ? (0.0F) : (1.0F / (a)))'),)
+             f'(((a) < {eps}F || ({b}) < {eps}F) ? (0.0F) : ((1.0F) / (a)))'),), {}
 
 
 @iet_pass
diff --git a/devito/passes/iet/orchestration.py b/devito/passes/iet/orchestration.py
index b807fd561b..cd3cbf17b3 100644
--- a/devito/passes/iet/orchestration.py
+++ b/devito/passes/iet/orchestration.py
@@ -24,7 +24,7 @@ class Orchestrator:
     Lower the SyncSpot in IET for efficient host-device asynchronous computation.
     """
 
-    lang = LangBB
+    langbb = LangBB
     """
     The language used to implement host-device data movements.
     """
@@ -55,7 +55,7 @@ def _make_releaselock(self, iet, sync_ops, *args):
         return iet, [efunc]
 
     def _make_withlock(self, iet, sync_ops, layer):
-        body, prefix = withlock(layer, iet, sync_ops, self.lang, self.sregistry)
+        body, prefix = withlock(layer, iet, sync_ops, self.langbb, self.sregistry)
 
         # Turn `iet` into an AsyncCallable so that subsequent passes know
         # that we're happy for this Callable to be executed asynchronously
@@ -94,7 +94,7 @@ def _make_syncarray(self, iet, sync_ops, layer):
 
         body = list(iet.body)
         try:
-            body.extend([self.lang._map_update_device(s.target, s.imask, qid=qid)
+            body.extend([self.langbb._map_update_device(s.target, s.imask, qid=qid)
                          for s in sync_ops])
         except NotImplementedError:
             pass
@@ -103,7 +103,7 @@ def _make_syncarray(self, iet, sync_ops, layer):
         return iet, []
 
     def _make_prefetchupdate(self, iet, sync_ops, layer):
-        body, prefix = prefetchupdate(layer, iet, sync_ops, self.lang, self.sregistry)
+        body, prefix = prefetchupdate(layer, iet, sync_ops, self.langbb, self.sregistry)
 
         # Turn `iet` into an AsyncCallable so that subsequent passes know
         # that we're happy for this Callable to be executed asynchronously
diff --git a/devito/passes/iet/parpragma.py b/devito/passes/iet/parpragma.py
index c3ed016a94..9cefc4786b 100644
--- a/devito/passes/iet/parpragma.py
+++ b/devito/passes/iet/parpragma.py
@@ -9,12 +9,12 @@
 from devito.ir import (Conditional, DummyEq, Dereference, Expression,
                        ExpressionBundle, FindSymbols, FindNodes, ParallelIteration,
                        ParallelTree, Pragma, Prodder, Transfer, List, Transformer,
-                       IsPerfectIteration, OpInc, filter_iterations,
+                       IsPerfectIteration, OpInc, filter_iterations, ccode,
                        retrieve_iteration_tree, IMask, VECTORIZED)
 from devito.passes.iet.engine import iet_pass
 from devito.passes.iet.langbase import (LangBB, LangTransformer, DeviceAwareMixin,
                                         ShmTransformer, make_sections_from_imask)
-from devito.symbolics import INT, ccode
+from devito.symbolics import INT
 from devito.tools import as_tuple, flatten, is_integer, prod
 from devito.types import Symbol
 
@@ -50,10 +50,10 @@ def _make_simd_pragma(self, iet):
         indexeds = FindSymbols('indexeds').visit(iet)
         aligned = {i.base for i in indexeds if i.function.is_DiscreteFunction}
         if aligned:
-            simd = self.lang['simd-for-aligned']
+            simd = self.langbb['simd-for-aligned']
             simd = as_tuple(simd(self.simd_reg_nbytes, *aligned))
         else:
-            simd = as_tuple(self.lang['simd-for'])
+            simd = as_tuple(self.langbb['simd-for'])
 
         return simd
 
@@ -239,7 +239,7 @@ def _make_reductions(self, partree):
             mapper = {partree.root: partree.root._rebuild(reduction=reductions)}
         elif all(i is OpInc for _, _, i in reductions):
             # Use atomic increments
-            mapper = {i: i._rebuild(pragmas=self.lang['atomic']) for i in exprs}
+            mapper = {i: i._rebuild(pragmas=self.langbb['atomic']) for i in exprs}
         else:
             raise NotImplementedError
 
@@ -292,7 +292,7 @@ def _make_partree(self, candidates, nthreads=None):
                                       **root.args)
 
             niters = prod([root.symbolic_size] + [j.symbolic_size for j in collapsable])
-            value = INT(Max(niters / (nthreads*self.chunk_nonaffine), 1))
+            value = INT(Max(INT(niters / (nthreads*self.chunk_nonaffine)), 1))
             prefix = [Expression(DummyEq(chunk_size, value, dtype=np.int32))]
 
         # Create a ParallelTree
@@ -319,7 +319,7 @@ def _make_parregion(self, partree, parrays):
             vexpandeds.append(VExpanded(i, pi))
 
         if vexpandeds:
-            init = self.lang['thread-num'](retobj=self.threadid)
+            init = self.langbb['thread-num'](retobj=self.threadid)
             prefix = List(body=[init] + vexpandeds + list(partree.prefix),
                           footer=c.Line())
             partree = partree._rebuild(prefix=prefix)
@@ -417,7 +417,7 @@ def _make_parallel(self, iet, sync_mapper=None):
 
         iet = Transformer(mapper).visit(iet)
 
-        return iet, {'includes': [self.lang['header']]}
+        return iet, {'includes': [self.langbb['header']]}
 
     def make_parallel(self, graph):
         return self._make_parallel(graph, sync_mapper=graph.sync_mapper)
diff --git a/devito/symbolics/__init__.py b/devito/symbolics/__init__.py
index 0f5c261471..3f1525297a 100644
--- a/devito/symbolics/__init__.py
+++ b/devito/symbolics/__init__.py
@@ -1,6 +1,6 @@
 from devito.symbolics.extended_sympy import *  # noqa
+from devito.symbolics.extended_dtypes import *  # noqa
 from devito.symbolics.queries import *  # noqa
 from devito.symbolics.search import *  # noqa
-from devito.symbolics.printer import *  # noqa
 from devito.symbolics.inspection import *  # noqa
 from devito.symbolics.manipulation import *  # noqa
diff --git a/devito/symbolics/extended_dtypes.py b/devito/symbolics/extended_dtypes.py
new file mode 100644
index 0000000000..080ef09fbb
--- /dev/null
+++ b/devito/symbolics/extended_dtypes.py
@@ -0,0 +1,97 @@
+import ctypes
+import numpy as np
+
+from devito.symbolics.extended_sympy import ReservedWord, Cast, ValueLimit
+from devito.tools import (Bunch, float2, float3, float4, double2, double3, double4,  # noqa
+                          int2, int3, int4, ctypes_vector_mapper)
+from devito.tools.dtypes_lowering import dtype_mapper
+
+__all__ = ['cast', 'CustomType', 'limits_mapper', 'INT', 'FLOAT', 'BaseCast',  # noqa
+           'DOUBLE', 'VOID', 'NoDeclStruct', 'c_complex', 'c_double_complex']
+
+
+limits_mapper = {
+    np.int32: Bunch(min=ValueLimit('INT_MIN'), max=ValueLimit('INT_MAX')),
+    np.int64: Bunch(min=ValueLimit('LONG_MIN'), max=ValueLimit('LONG_MAX')),
+    np.float32: Bunch(min=-ValueLimit('FLT_MIN'), max=ValueLimit('FLT_MAX')),
+    np.float64: Bunch(min=-ValueLimit('DBL_MIN'), max=ValueLimit('DBL_MAX')),
+}
+
+
+class NoDeclStruct(ctypes.Structure):
+    """
+    A ctypes.Structure that does not generate a struct definition.
+
+    Some foreign types (e.g. complex) need to be passed to C/C++ as a struct
+    that mimics an existing type, but the struct types themselves don't show
+    up in the kernel, so we don't need to generate their definitions.
+    """
+
+    pass
+
+
+class c_complex(NoDeclStruct):
+    """
+    Structure for passing complex float to C/C++
+    """
+
+    _fields_ = [('real', ctypes.c_float), ('imag', ctypes.c_float)]
+
+    _base_dtype = True
+
+    @classmethod
+    def from_param(cls, val):
+        return cls(val.real, val.imag)
+
+
+class c_double_complex(NoDeclStruct):
+    """
+    Structure for passing complex double to C/C++
+    """
+
+    _fields_ = [('real', ctypes.c_double), ('imag', ctypes.c_double)]
+
+    _base_dtype = True
+
+    @classmethod
+    def from_param(cls, val):
+        return cls(val.real, val.imag)
+
+
+ctypes_vector_mapper.update({np.complex64: c_complex,
+                             np.complex128: c_double_complex})
+
+
+class CustomType(ReservedWord):
+    pass
+
+
+def cast(casttype, stars=None):
+    return lambda v, dtype=None, **kw: Cast(v, dtype=casttype, stars=stars, **kw)
+
+
+ULONG = cast(np.uint64)
+UINTP = cast(np.uint32, '*')
+
+
+# Standard ones, needed as class for e.g. single dispatch
+class BaseCast(Cast):
+
+    def __new__(cls, base, stars=None, **kwargs):
+        kwargs['dtype'] = cls._dtype
+        return super().__new__(cls, base, stars=stars, **kwargs)
+
+
+class VOID(BaseCast):
+
+    _dtype = 'void'
+
+
+# Dynamically create INT, INT2, .... INTP, INT2P, ... FLOAT, ...
+for (base_name, dtype) in dtype_mapper.items():
+    name = base_name.upper()
+    globals()[name] = type(name, (BaseCast,), {'_dtype': dtype})
+    for i in ['2', '3', '4']:
+        v = '%s%s' % (base_name, i)
+        globals()[v.upper()] = cast(v)
+        globals()[f'{v.upper()}P'] = cast(v, '*')
diff --git a/devito/symbolics/extended_sympy.py b/devito/symbolics/extended_sympy.py
index 5b13262ded..8acb5cac99 100644
--- a/devito/symbolics/extended_sympy.py
+++ b/devito/symbolics/extended_sympy.py
@@ -10,7 +10,8 @@
 from devito.finite_differences.elementary import Min, Max
 from devito.tools import (Pickable, Bunch, as_tuple, is_integer, float2,  # noqa
                           float3, float4, double2, double3, double4, int2, int3,
-                          int4)
+                          int4, dtype_to_ctype, ctypes_to_cstr, ctypes_vector_mapper,
+                          ctypes_to_cstr)
 from devito.types import Symbol
 from devito.types.basic import Basic
 
@@ -18,9 +19,8 @@
            'CallFromComposite', 'FieldFromPointer', 'FieldFromComposite',
            'ListInitializer', 'Byref', 'IndexedPointer', 'Cast', 'DefFunction',
            'MathFunction', 'InlineIf', 'ReservedWord', 'Keyword', 'String',
-           'Macro', 'Class', 'MacroArgument', 'CustomType', 'Deref', 'Namespace',
-           'Rvalue', 'INT', 'FLOAT', 'DOUBLE', 'VOID', 'Null', 'SizeOf', 'rfunc',
-           'cast_mapper', 'BasicWrapperMixin', 'ValueLimit', 'limits_mapper']
+           'Macro', 'Class', 'MacroArgument', 'Deref', 'Namespace',
+           'Rvalue', 'Null', 'SizeOf', 'rfunc', 'BasicWrapperMixin', 'ValueLimit']
 
 
 class CondEq(sympy.Eq):
@@ -89,10 +89,10 @@ def __new__(cls, lhs, rhs, params=None):
         if not is_integer(rhs):
             # Perhaps it's a symbolic RHS -- but we wanna be sure it's of type int
             if not hasattr(rhs, 'dtype'):
-                raise ValueError("Symbolic RHS `%s` lacks dtype" % rhs)
+                raise ValueError(f"Symbolic RHS `{rhs}` lacks dtype")
             if not issubclass(rhs.dtype, np.integer):
-                raise ValueError("Symbolic RHS `%s` must be of type `int`, found "
-                                 "`%s` instead" % (rhs, rhs.dtype))
+                raise ValueError(f"Symbolic RHS `{rhs}` must be of type `int`, found "
+                                 f"`{rhs.dtype}` instead")
         rhs = sympify(rhs)
 
         obj = sympy.Expr.__new__(cls, lhs, rhs)
@@ -103,7 +103,7 @@ def __new__(cls, lhs, rhs, params=None):
         return obj
 
     def __str__(self):
-        return "IntDiv(%s, %s)" % (self.lhs, self.rhs)
+        return f"IntDiv({self.lhs}, {self.rhs})"
 
     __repr__ = __str__
 
@@ -191,8 +191,8 @@ def __new__(cls, call, pointer, params=None, **kwargs):
         return obj
 
     def __str__(self):
-        return '%s->%s(%s)' % (self.pointer, self.call,
-                               ", ".join(str(i) for i in as_tuple(self.params)))
+        params = ", ".join(str(i) for i in as_tuple(self.params))
+        return f'{self.pointer}->{self.call}({params})'
 
     __repr__ = __str__
 
@@ -228,8 +228,8 @@ class CallFromComposite(CallFromPointer, Pickable):
     """
 
     def __str__(self):
-        return '%s.%s(%s)' % (self.pointer, self.call,
-                              ", ".join(str(i) for i in as_tuple(self.params)))
+        params = ", ".join(str(i) for i in as_tuple(self.params))
+        return f'{self.pointer}.{self.call}({params})'
 
     __repr__ = __str__
 
@@ -246,7 +246,7 @@ def __new__(cls, field, pointer, *args, **kwargs):
         return CallFromPointer.__new__(cls, field, pointer)
 
     def __str__(self):
-        return '%s->%s' % (self.pointer, self.field)
+        return f'{self.pointer}->{self.field}'
 
     @property
     def field(self):
@@ -268,7 +268,7 @@ def __new__(cls, field, composite, *args, **kwargs):
         return CallFromPointer.__new__(cls, field, composite)
 
     def __str__(self):
-        return '%s.%s' % (self.composite, self.field)
+        return f'{self.composite}.{self.field}'
 
     @property
     def field(self):
@@ -295,13 +295,13 @@ def __new__(cls, params):
             try:
                 args.append(sympify(p))
             except sympy.SympifyError:
-                raise ValueError("Illegal param `%s`" % p)
+                raise ValueError(f"Illegal param `{p}`")
         obj = sympy.Expr.__new__(cls, *args)
         obj.params = tuple(args)
         return obj
 
     def __str__(self):
-        return "{%s}" % ", ".join(str(i) for i in self.params)
+        return f"{{{', '.join(str(i) for i in self.params)}}}"
 
     __repr__ = __str__
 
@@ -349,9 +349,9 @@ def free_symbols(self):
 
     def __str__(self):
         if self.base.is_Symbol:
-            return "%s%s" % (self._op, str(self.base))
+            return f'{self._op}{self.base}'
         else:
-            return "%s(%s)" % (self._op, str(self.base))
+            return f'{self._op}({self.base})'
 
     __repr__ = __str__
 
@@ -383,24 +383,22 @@ class Cast(UnaryOp):
     Symbolic representation of the C notation `(type)expr`.
     """
 
-    _base_typ = ''
+    __rargs__ = ('base', )
+    __rkwargs__ = ('dtype', 'stars', 'reinterpret')
 
-    __rkwargs__ = ('stars',)
-
-    def __new__(cls, base, stars=None, **kwargs):
-        # Attempt simplifcation
-        # E.g., `FLOAT(32) -> 32.0` of type `sympy.Float`
+    def __new__(cls, base, dtype=None, stars=None, reinterpret=False, **kwargs):
         try:
-            return sympify(eval(cls._base_typ)(base))
-        except (NameError, SyntaxError):
-            # E.g., `_base_typ` is "char" or "unsigned long"
-            pass
+            if issubclass(dtype, np.generic) and sympify(base).is_Number:
+                base = sympify(dtype(base))
         except TypeError:
-            # `base` ain't a number
+            # E.g. void
             pass
 
         obj = super().__new__(cls, base)
-        obj._stars = stars
+        obj._stars = stars or ''
+        obj._dtype = dtype
+        obj._reinterpret = reinterpret
+
         return obj
 
     def _hashable_content(self):
@@ -413,12 +411,28 @@ def stars(self):
         return self._stars
 
     @property
-    def typ(self):
-        return '%s%s' % (self._base_typ, self.stars or '')
+    def dtype(self):
+        return self._dtype
+
+    @property
+    def reinterpret(self):
+        return self._reinterpret
+
+    @property
+    def _C_ctype(self):
+        ctype = ctypes_vector_mapper.get(self.dtype, self.dtype)
+        try:
+            ctype = dtype_to_ctype(ctype)
+        except TypeError:
+            pass
+        return ctype
 
     @property
     def _op(self):
-        return '(%s)' % self.typ
+        return f'({ctypes_to_cstr(self._C_ctype)})'
+
+    def __str__(self):
+        return f"{self._op}{self.base}"
 
 
 class IndexedPointer(sympy.Expr, Pickable, BasicWrapperMixin):
@@ -457,7 +471,8 @@ def index(self):
         return self._index
 
     def __str__(self):
-        return "%s%s" % (self.base, ''.join('[%s]' % i for i in self.index))
+        indices = ''.join(f'[{i}]' for i in self.index)
+        return f"{self.base}{indices}"
 
     __repr__ = __str__
 
@@ -484,7 +499,7 @@ class ReservedWord(sympy.Atom, Pickable):
 
     def __new__(cls, value, **kwargs):
         if not isinstance(value, str):
-            raise TypeError("Expected str, got `%s`" % type(value))
+            raise TypeError(f"Expected str, got `{type(value)}`")
         obj = sympy.Atom.__new__(cls, **kwargs)
         obj.value = value
 
@@ -509,10 +524,6 @@ class Keyword(ReservedWord):
     pass
 
 
-class CustomType(ReservedWord):
-    pass
-
-
 class String(ReservedWord):
     pass
 
@@ -524,7 +535,7 @@ class Macro(ReservedWord):
 class Class(ReservedWord):
 
     def __str__(self):
-        return "class %s" % self.value
+        return f"class {self.value}"
 
     __repr__ = __str__
 
@@ -532,7 +543,7 @@ def __str__(self):
 class MacroArgument(sympy.Symbol):
 
     def __str__(self):
-        return "(%s)" % self.name
+        return f"({self.name})"
 
     __repr__ = __str__
 
@@ -547,14 +558,6 @@ class ValueLimit(ReservedWord, sympy.Expr):
     pass
 
 
-limits_mapper = {
-    np.int32: Bunch(min=ValueLimit('INT_MIN'), max=ValueLimit('INT_MAX')),
-    np.int64: Bunch(min=ValueLimit('LONG_MIN'), max=ValueLimit('LONG_MAX')),
-    np.float32: Bunch(min=-ValueLimit('FLT_MAX'), max=ValueLimit('FLT_MAX')),
-    np.float64: Bunch(min=-ValueLimit('DBL_MAX'), max=ValueLimit('DBL_MAX')),
-}
-
-
 class DefFunction(Function, Pickable):
 
     """
@@ -613,11 +616,11 @@ def template(self):
 
     def __str__(self):
         if self.template:
-            template = '<%s>' % ','.join(str(i) for i in self.template)
+            template = f"<{','.join(str(i) for i in self.template)}>"
         else:
             template = ''
         arguments = ', '.join(str(i) for i in self.arguments)
-        return "%s%s(%s)" % (self.name, template, arguments)
+        return f"{self.name}{template}({arguments})"
 
     __repr__ = __str__
 
@@ -670,7 +673,7 @@ def false_expr(self):
         return self._false_expr
 
     def __str__(self):
-        return "(%s) ? %s : %s" % (self.cond, self.true_expr, self.false_expr)
+        return f"({self.cond}) ? {self.true_expr} : {self.false_expr}"
 
     __repr__ = __str__
 
@@ -754,125 +757,43 @@ def init(self):
     def __str__(self):
         rvalue = str(self.expr)
         if self.namespace:
-            rvalue = "%s::%s" % (self.namespace, rvalue)
+            rvalue = f"{self.namespace}::{rvalue}"
         if self.init:
-            rvalue = "%s%s" % (rvalue, self.init)
+            rvalue = f"{rvalue}{self.init}"
         return rvalue
 
     __repr__ = __str__
 
 
-# *** Casting
-
-class CastStar:
-
-    base = None
-
-    def __new__(cls, base=''):
-        return cls.base(base, '*')
-
-
-# Dynamically create INT, INT2, .... INTP, INT2P, ... FLOAT, ...
-for base_name in ['int', 'float', 'double']:
-    for i in ['', '2', '3', '4']:
-        v = '%s%s' % (base_name, i)
-        cls = type(v.upper(), (Cast,), {'_base_typ': v})
-        globals()[cls.__name__] = cls
-
-        clsp = type('%sP' % v.upper(), (CastStar,), {'base': cls})
-        globals()[clsp.__name__] = clsp
-
-
-class CHAR(Cast):
-    _base_typ = 'char'
-
-
-class SHORT(Cast):
-    _base_typ = 'short'
-
-
-class USHORT(Cast):
-    _base_typ = 'unsigned short'
-
-
-class UCHAR(Cast):
-    _base_typ = 'unsigned char'
-
-
-class UINT(Cast):
-    _base_typ = 'unsigned int'
-
-
-class UINTP(CastStar):
-    base = UINT
-
-
-class LONG(Cast):
-    _base_typ = 'long'
-
-
-class ULONG(Cast):
-    _base_typ = 'unsigned long'
-
-
-class VOID(Cast):
-    _base_typ = 'void'
-
-
-class CHARP(CastStar):
-    base = CHAR
-
-
-class UCHARP(CastStar):
-    base = UCHAR
-
-
-class SHORTP(CastStar):
-    base = SHORT
-
-
-class USHORTP(CastStar):
-    base = USHORT
-
+# Some other utility objects
+Null = Macro('NULL')
 
-cast_mapper = {
-    np.int8: CHAR,
-    np.uint8: UCHAR,
-    np.int16: SHORT,  # noqa
-    np.uint16: USHORT,  # noqa
-    int: INT,  # noqa
-    np.int32: INT,  # noqa
-    np.int64: LONG,
-    np.uint64: ULONG,
-    np.float32: FLOAT,  # noqa
-    float: DOUBLE,  # noqa
-    np.float64: DOUBLE,  # noqa
 
-    (np.int8, '*'): CHARP,
-    (np.uint8, '*'): UCHARP,
-    (int, '*'): INTP,  # noqa
-    (np.uint16, '*'): USHORTP,  # noqa
-    (np.int16, '*'): SHORTP,  # noqa
-    (np.int32, '*'): INTP,  # noqa
-    (np.int64, '*'): INTP,  # noqa
-    (np.float32, '*'): FLOATP,  # noqa
-    (float, '*'): DOUBLEP,  # noqa
-    (np.float64, '*'): DOUBLEP  # noqa
-}
+# DefFunction, unlike sympy.Function, generates e.g. `sizeof(float)`, not `sizeof(float_)`
+class SizeOf(DefFunction):
+
+    __rargs__ = ('intype', 'stars')
+
+    def __new__(cls, intype, stars=None, **kwargs):
+        stars = stars or ''
+        if not isinstance(intype, (str, ReservedWord)):
+            ctype = dtype_to_ctype(intype)
+            for k, v in ctypes_vector_mapper.items():
+                if ctype is v:
+                    intype = k
+                    break
+            else:
+                intype = ctypes_to_cstr(ctype)
 
-for base_name in ['int', 'float', 'double']:
-    for i in [2, 3, 4]:
-        v = '%s%d' % (base_name, i)
-        cls = locals()[v]
-        cast_mapper[cls] = locals()[v.upper()]
-        cast_mapper[(cls, '*')] = locals()['%sP' % v.upper()]
+        newobj = super().__new__(cls, 'sizeof', arguments=f'{intype}{stars}', **kwargs)
+        newobj.stars = stars
+        newobj.intype = intype
 
+        return newobj
 
-# Some other utility objects
-Null = Macro('NULL')
-
-# DefFunction, unlike sympy.Function, generates e.g. `sizeof(float)`, not `sizeof(float_)`
-SizeOf = lambda *args: DefFunction('sizeof', tuple(args))
+    @property
+    def args(self):
+        return super().args[1]
 
 
 def rfunc(func, item, *args):
diff --git a/devito/symbolics/inspection.py b/devito/symbolics/inspection.py
index 18e2623764..0ff7fcf6ba 100644
--- a/devito/symbolics/inspection.py
+++ b/devito/symbolics/inspection.py
@@ -3,11 +3,13 @@
 import numpy as np
 from sympy import (Function, Indexed, Integer, Mul, Number,
                    Pow, S, Symbol, Tuple)
+from sympy.core.numbers import ImaginaryUnit
 
 from devito.finite_differences import Derivative
 from devito.finite_differences.differentiable import IndexDerivative
 from devito.logger import warning
-from devito.symbolics.extended_sympy import (INT, CallFromPointer, Cast,
+from devito.symbolics.extended_dtypes import INT
+from devito.symbolics.extended_sympy import (CallFromPointer, Cast,
                                              DefFunction, ReservedWord)
 from devito.symbolics.queries import q_routine
 from devito.tools import as_tuple, prod
@@ -142,6 +144,8 @@ def wrapper(expr, estimate, seen):
 def _estimate_cost(expr, estimate, seen):
     # Retval: flops (int), flag (bool)
     # The flag tells wether it's an integer expression (implying flops==0) or not
+    if not expr.args:
+        return 0, False
     flops, flags = zip(*[_estimate_cost(a, estimate, seen) for a in expr.args])
     flops = sum(flops)
     if all(flags):
@@ -168,6 +172,7 @@ def _(expr, estimate, seen):
     return 0, True
 
 
+@_estimate_cost.register(ImaginaryUnit)
 @_estimate_cost.register(Number)
 @_estimate_cost.register(ReservedWord)
 def _(expr, estimate, seen):
@@ -190,6 +195,8 @@ def _(expr, estimate, seen):
     flops, flags = _estimate_cost.registry[object](expr, estimate, seen)
     if {S.One, S.NegativeOne}.intersection(expr.args):
         flops -= 1
+    if ImaginaryUnit in expr.args:
+        flops *= 2
     return flops, flags
 
 
@@ -282,7 +289,9 @@ def has_integer_args(*args):
     res = True
     for a in args:
         try:
-            if len(a.args) > 0:
+            if isinstance(a, INT):
+                res = res and True
+            elif len(a.args) > 0:
                 res = res and has_integer_args(*a.args)
             else:
                 res = res and has_integer_args(a)
@@ -291,14 +300,31 @@ def has_integer_args(*args):
     return res
 
 
-def sympy_dtype(expr, base=None):
+def sympy_dtype(expr, base=None, default=None, smin=None):
     """
     Infer the dtype of the expression.
     """
+    if expr is None:
+        return default
+
     dtypes = {base} - {None}
     for i in expr.free_symbols:
         try:
             dtypes.add(i.dtype)
         except AttributeError:
             pass
-    return infer_dtype(dtypes)
+
+    dtype = infer_dtype(dtypes)
+
+    # Promote if we missed complex number, i.e f + I
+    is_im = np.issubdtype(dtype, np.complexfloating)
+    if expr.has(ImaginaryUnit) and not is_im:
+        if dtype is None:
+            dtype = default or np.complex64
+        else:
+            dtype = np.promote_types(dtype, np.complex64).type
+
+    if smin is not None and not np.issubdtype(dtype, np.integer):
+        dtype = np.promote_types(dtype, smin).type
+
+    return dtype
diff --git a/devito/symbolics/manipulation.py b/devito/symbolics/manipulation.py
index f5992ac8be..bf795cb86d 100644
--- a/devito/symbolics/manipulation.py
+++ b/devito/symbolics/manipulation.py
@@ -13,6 +13,7 @@
 from devito.symbolics.extended_sympy import DefFunction, rfunc
 from devito.symbolics.queries import q_leaf
 from devito.symbolics.search import retrieve_indexed, retrieve_functions
+from devito.symbolics.unevaluation import Mul as UMul
 from devito.tools import as_list, as_tuple, flatten, split, transitive_closure
 from devito.types.basic import Basic, Indexed
 from devito.types.array import ComponentAccess
@@ -128,7 +129,10 @@ def _(mapper, rule):
 
 @singledispatch
 def _uxreplace_handle(expr, args, kwargs):
-    return expr.func(*args)
+    try:
+        return expr.func(*args, evaluate=False)
+    except TypeError:
+        return expr.func(*args)
 
 
 @_uxreplace_handle.register(Min)
@@ -329,24 +333,25 @@ def pow_to_mul(expr):
         if exp > 10 or exp < -10 or exp == 0:
             # Large powers remain untouched
             return expr
-        elif exp == -1 or (int(exp) - exp != 0):
-            # Reciprocals and fractional powers also remain untouched,
+        elif (int(exp) - exp != 0):
+            # Fractional powers also remain untouched,
             # but at least we traverse the base looking for other Pows
             return expr.func(pow_to_mul(base), exp, evaluate=False)
         elif exp > 0:
-            return Mul(*[pow_to_mul(base)]*int(exp), evaluate=False)
+            return UMul(*[pow_to_mul(base)]*int(exp), evaluate=False)
+        elif exp < 0:
+            # Reciprocal powers become inverse of the negative power
+            # for example Pow(expr, -2) becomes Pow(expr * expr, -1)
+            return expr.func(pow_to_mul(base**(-int(exp))), -1, evaluate=False)
         else:
-            # SymPy represents 1/x as Pow(x,-1). Also, it represents
-            # 2/x as Mul(2, Pow(x, -1)). So we shouldn't end up here,
-            # but just in case SymPy changes its internal conventions...
-            posexpr = Mul(*[base]*(-int(exp)), evaluate=False)
-            return Pow(posexpr, -1, evaluate=False)
+            # Default. We should not end up here as all cases are handled
+            return expr
     else:
         args = [pow_to_mul(i) for i in expr.args]
 
         # Some SymPy versions will evaluate the two-args case
         # `(negative integer, mul)` despite the `evaluate=False`. For example,
-        # `Mul(-2, a*a, evaluate=False)` gets evaluated to `-2/a**2`. By swapping
+        # `Mul(-2, 1/a*a, evaluate=False)` gets evaluated to `-2/a**2`. By swapping
         # the args, the issue disappears...
         try:
             a0, a1 = args
@@ -393,7 +398,7 @@ def normalize_args(args):
     for k, v in args.items():
         try:
             retval[k] = sympify(v, strict=True)
-        except SympifyError:
+        except (TypeError, SympifyError):
             continue
 
     return retval
diff --git a/devito/tools/data_structures.py b/devito/tools/data_structures.py
index de7aa69d0d..c32360b6a7 100644
--- a/devito/tools/data_structures.py
+++ b/devito/tools/data_structures.py
@@ -267,6 +267,11 @@ def update(self, *args, **kwargs):
             for e in s:
                 self.add(e)
 
+    def union(self, *args):
+        ret = OrderedSet(*self)
+        ret.update(*args)
+        return ret
+
     def add(self, elem):
         self[elem] = None
 
@@ -299,7 +304,6 @@ def __str__(self):
     issuperset = property(lambda self: self.__ge__)
     symmetric_difference = property(lambda self: self.__xor__)
     symmetric_difference_update = property(lambda self: self.__ixor__)
-    union = property(lambda self: self.__or__)
 
 
 class Ordering(tuple):
diff --git a/devito/tools/dtypes_lowering.py b/devito/tools/dtypes_lowering.py
index b5b564a4d7..8f3111ccaa 100644
--- a/devito/tools/dtypes_lowering.py
+++ b/devito/tools/dtypes_lowering.py
@@ -3,6 +3,8 @@
 """
 
 import ctypes
+from functools import reduce
+from operator import mul
 
 import numpy as np
 from cgen import dtype_to_ctype as cgen_dtype_to_ctype
@@ -11,24 +13,25 @@
 
 __all__ = ['int2', 'int3', 'int4', 'float2', 'float3', 'float4', 'double2',  # noqa
            'double3', 'double4', 'dtypes_vector_mapper', 'dtype_to_mpidtype',
-           'dtype_to_cstr', 'dtype_to_ctype', 'dtype_to_mpitype', 'dtype_len',
-           'ctypes_to_cstr', 'c_restrict_void_p', 'ctypes_vector_mapper',
-           'is_external_ctype', 'infer_dtype', 'CustomDtype']
+           'dtype_to_cstr', 'dtype_to_ctype', 'infer_datasize', 'dtype_to_mpitype',
+           'dtype_len', 'ctypes_to_cstr', 'c_restrict_void_p', 'ctypes_vector_mapper',
+           'is_external_ctype', 'infer_dtype', 'CustomDtype', 'mpi4py_mapper']
 
 
 # *** Custom np.dtypes
 
 # NOTE: the following is inspired by pyopencl.cltypes
 
-mapper = {
+dtype_mapper = {
     "int": np.int32,
     "float": np.float32,
     "double": np.float64
 }
 
 
-def build_dtypes_vector(field_names, counts):
+def build_dtypes_vector(field_names, counts, mapper=None):
     ret = {}
+    mapper = mapper or dtype_mapper
     for base_name, base_dtype in mapper.items():
         for count in counts:
             name = "%s%d" % (base_name, count)
@@ -92,7 +95,7 @@ def get_base_dtype(self, v, default=None):
 # Standard vector dtypes
 dtypes_vector_mapper.update(build_dtypes_vector(field_names, counts))
 # Fallbacks
-dtypes_vector_mapper.update({(v, 1): v for v in mapper.values()})
+dtypes_vector_mapper.update({(v, 1): v for v in dtype_mapper.values()})
 
 
 # *** Custom types escaping both the numpy and ctypes namespaces
@@ -133,6 +136,9 @@ def dtype_to_cstr(dtype):
 
 def dtype_to_ctype(dtype):
     """Translate numpy.dtype into a ctypes type."""
+    if isinstance(dtype, CustomDtype):
+        return dtype
+
     try:
         return ctypes_vector_mapper[dtype]
     except KeyError:
@@ -140,7 +146,7 @@ def dtype_to_ctype(dtype):
 
     if isinstance(dtype, CustomDtype):
         return dtype
-    elif issubclass(dtype, ctypes._SimpleCData):
+    elif issubclass(dtype, (ctypes._Pointer, ctypes.Structure, ctypes._SimpleCData)):
         # Bypass np.ctypeslib's normalization rules such as
         # `np.ctypeslib.as_ctypes_type(ctypes.c_void_p) -> ctypes.c_ulong`
         return dtype
@@ -148,20 +154,53 @@ def dtype_to_ctype(dtype):
         return np.ctypeslib.as_ctypes_type(dtype)
 
 
+def infer_datasize(dtype, shape):
+    """
+    Translate numpy.dtype to (ctype, int): type and scale for correct C allocation size.
+    """
+    datasize = int(reduce(mul, shape))
+    if isinstance(dtype, CustomDtype):
+        return dtype, datasize
+
+    try:
+        return ctypes_vector_mapper[dtype], datasize
+    except KeyError:
+        pass
+
+    if issubclass(dtype, ctypes._SimpleCData):
+        return dtype, datasize
+
+    if dtype == np.float16:
+        # Allocate half float as unsigned short
+        return ctypes.c_uint16, datasize
+
+    if np.issubdtype(dtype, np.complexfloating):
+        # For complex float, allocate twice the size of real/imaginary part
+        return np.ctypeslib.as_ctypes_type(dtype(0).real.__class__), 2 * datasize
+
+    return np.ctypeslib.as_ctypes_type(dtype), datasize
+
+
+mpi4py_mapper = {}
+mpi_mapper = {
+    np.ubyte: 'MPI_BYTE',
+    np.ushort: 'MPI_UNSIGNED_SHORT',
+    np.int32: 'MPI_INT',
+    np.float32: 'MPI_FLOAT',
+    np.int64: 'MPI_LONG',
+    np.float64: 'MPI_DOUBLE',
+    np.complex64: 'MPI_C_COMPLEX',
+    np.complex128: 'MPI_C_DOUBLE_COMPLEX'
+}
+
+
 def dtype_to_mpitype(dtype):
     """Map numpy types to MPI datatypes."""
 
     # Resolve vector dtype if necessary
     dtype = dtypes_vector_mapper.get_base_dtype(dtype)
 
-    return {
-        np.ubyte: 'MPI_BYTE',
-        np.ushort: 'MPI_UNSIGNED_SHORT',
-        np.int32: 'MPI_INT',
-        np.float32: 'MPI_FLOAT',
-        np.int64: 'MPI_LONG',
-        np.float64: 'MPI_DOUBLE'
-    }[dtype]
+    return mpi_mapper[dtype]
 
 
 def dtype_to_mpidtype(dtype):
@@ -192,7 +231,7 @@ class c_restrict_void_p(ctypes.c_void_p):
 
 
 ctypes_vector_mapper = {}
-for base_name, base_dtype in mapper.items():
+for base_name, base_dtype in dtype_mapper.items():
     base_ctype = dtype_to_ctype(base_dtype)
 
     for count in counts:
@@ -200,7 +239,8 @@ class c_restrict_void_p(ctypes.c_void_p):
 
         name = "%s%d" % (base_name, count)
         ctype = type(name, (ctypes.Structure,),
-                     {'_fields_': [(i, base_ctype)] for i in field_names[:count]})
+                     {'_fields_': [(i, base_ctype) for i in field_names[:count]],
+                      '_base_dtype': True})
 
         ctypes_vector_mapper[dtype] = ctype
 
@@ -232,7 +272,6 @@ def ctypes_to_cstr(ctype, toarray=None):
         retval = '%s[%d]' % (ctypes_to_cstr(ctype._type_, toarray), ctype._length_)
     elif ctype.__name__.startswith('c_'):
         name = ctype.__name__[2:]
-
         # A primitive datatype
         # FIXME: Is there a better way of extracting the C typename ?
         # Here, we're following the ctypes convention that each basic type has
@@ -268,11 +307,6 @@ def ctypes_to_cstr(ctype, toarray=None):
     return retval
 
 
-known_ctypes = {
-    'vector_types.h': list(ctypes_vector_mapper.values()),
-}
-
-
 def is_external_ctype(ctype, includes):
     """
     True if `ctype` is known to be declared in one of the given `includes`
@@ -285,13 +319,22 @@ def is_external_ctype(ctype, includes):
     if issubclass(ctype, ctypes._SimpleCData):
         return False
 
-    for k, v in known_ctypes.items():
-        if ctype in v:
-            return True
+    if ctype in ctypes_vector_mapper.values():
+        return True
 
     return False
 
 
+def is_numpy_dtype(dtype):
+    """
+    True if `dtype` is a numpy dtype, False otherwise.
+    """
+    try:
+        return issubclass(dtype, np.generic)
+    except TypeError:
+        return False
+
+
 def infer_dtype(dtypes):
     """
     Given a set of np.dtypes, return the "winning" dtype:
@@ -302,8 +345,11 @@ def infer_dtype(dtypes):
     """
     # Resolve the vector types, if any
     dtypes = {dtypes_vector_mapper.get_base_dtype(i, i) for i in dtypes}
-
-    fdtypes = {i for i in dtypes if np.issubdtype(i, np.floating)}
+    # Only keep number types
+    dtypes = {i for i in dtypes if is_numpy_dtype(i)}
+    # Separate floating point types from the rest
+    fdtypes = {i for i in dtypes if np.issubdtype(i, np.floating) or
+               np.issubdtype(i, np.complexfloating)}
     if len(fdtypes) > 1:
         return max(fdtypes, key=lambda i: np.dtype(i).itemsize)
     elif len(fdtypes) == 1:
diff --git a/devito/types/array.py b/devito/types/array.py
index 7eda353b9b..105cdd21da 100644
--- a/devito/types/array.py
+++ b/devito/types/array.py
@@ -365,6 +365,9 @@ class Bundle(ArrayBasic):
 
     __rkwargs__ = AbstractFunction.__rkwargs__ + ('components',)
 
+    def __new__(cls, *args, components=(), **kwargs):
+        return super().__new__(cls, *args, components=as_tuple(components), **kwargs)
+
     def __init_finalize__(self, *args, components=(), **kwargs):
         super().__init_finalize__(*args, components=components, **kwargs)
 
diff --git a/devito/types/basic.py b/devito/types/basic.py
index bd847f3a98..04e4281f12 100644
--- a/devito/types/basic.py
+++ b/devito/types/basic.py
@@ -1,7 +1,7 @@
 import abc
 import inspect
 from collections import namedtuple
-from ctypes import POINTER, _Pointer, c_char_p, c_char
+from ctypes import POINTER, _Pointer, c_char_p, c_char, Structure
 from functools import reduce, cached_property
 from operator import mul
 
@@ -13,8 +13,8 @@
 
 from devito.data import default_allocator
 from devito.parameters import configuration
-from devito.tools import (Pickable, as_tuple, ctypes_to_cstr, dtype_to_ctype,
-                          frozendict, memoized_meth, sympy_mutex)
+from devito.tools import (Pickable, as_tuple, dtype_to_ctype,
+                          frozendict, memoized_meth, sympy_mutex, CustomDtype)
 from devito.types.args import ArgProvider
 from devito.types.caching import Cached, Uncached
 from devito.types.lazy import Evaluable
@@ -81,9 +81,12 @@ def _C_name(self):
     @property
     def _C_typedata(self):
         """
-        The type of the object in the generated code as a `str`.
+        The type of the object's data in the generated code.
         """
         _type = self._C_ctype
+        if isinstance(_type, CustomDtype):
+            return _type
+
         while issubclass(_type, _Pointer):
             _type = _type._type_
 
@@ -91,7 +94,15 @@ def _C_typedata(self):
         if _type is c_char_p:
             _type = c_char
 
-        return ctypes_to_cstr(_type)
+        try:
+            # We have internal types such as c_complex that are
+            # Structure too but should be treated as plain c_type
+            _type._base_dtype
+        except AttributeError:
+            if issubclass(_type, Structure):
+                _type = f'struct {_type.__name__}'
+
+        return _type
 
     @abc.abstractproperty
     def _C_ctype(self):
@@ -338,8 +349,6 @@ class AbstractSymbol(sympy.Symbol, Basic, Pickable, Evaluable):
     is_Symbol = True
 
     # SymPy default assumptions
-    is_real = True
-    is_imaginary = False
     is_commutative = True
 
     __rkwargs__ = ('name', 'dtype', 'is_const')
@@ -400,6 +409,12 @@ def _hashable_content(self):
     def dtype(self):
         return self._dtype
 
+    def _eval_is_real(self):
+        return not self.is_imaginary
+
+    def _eval_is_imaginary(self):
+        return np.iscomplexobj(self.dtype(0))
+
     @property
     def indices(self):
         return ()
@@ -848,8 +863,6 @@ class AbstractFunction(sympy.Function, Basic, Pickable, Evaluable):
     is_AbstractFunction = True
 
     # SymPy default assumptions
-    is_real = True
-    is_imaginary = False
     is_commutative = True
 
     # Devito default assumptions
@@ -945,6 +958,8 @@ def _sympystr(self, printer, **kwargs):
         return str(self)
 
     _latex = _sympystr
+    _eval_is_real = AbstractSymbol._eval_is_real
+    _eval_is_imaginary = AbstractSymbol._eval_is_imaginary
 
     def _pretty(self, printer, **kwargs):
         return printer._print_Function(self, func_name=self.name)
diff --git a/devito/types/dense.py b/devito/types/dense.py
index 9af238a6d0..efdb22cca5 100644
--- a/devito/types/dense.py
+++ b/devito/types/dense.py
@@ -21,7 +21,8 @@
 from devito.finite_differences import Differentiable, generate_fd_shortcuts
 from devito.finite_differences.tools import fd_weights_registry
 from devito.tools import (ReducerMap, as_tuple, c_restrict_void_p, flatten,
-                          is_integer, memoized_meth, dtype_to_ctype, humanbytes)
+                          is_integer, memoized_meth, dtype_to_ctype, humanbytes,
+                          mpi4py_mapper)
 from devito.types.dimension import Dimension
 from devito.types.args import ArgProvider
 from devito.types.caching import CacheManager
@@ -786,6 +787,7 @@ def _halo_exchange(self):
 
         neighborhood = self._distributor.neighborhood
         comm = self._distributor.comm
+        comm_dtype = mpi4py_mapper.get(self.dtype, self.dtype)
 
         for d in self._dist_dimensions:
             for i in [LEFT, RIGHT]:
@@ -795,18 +797,18 @@ def _halo_exchange(self):
 
                 # Gather send data
                 data = self._data_in_region(OWNED, d, i)
-                sendbuf = np.ascontiguousarray(data)
+                sendbuf = np.ascontiguousarray(data.view(comm_dtype))
 
                 # Setup recv buffer
                 shape = self._data_in_region(HALO, d, i.flip()).shape
-                recvbuf = np.ndarray(shape=shape, dtype=self.dtype)
+                recvbuf = np.ndarray(shape=shape, dtype=comm_dtype)
 
                 # Communication
                 comm.Sendrecv(sendbuf, dest=dest, recvbuf=recvbuf, source=source)
 
                 # Scatter received data
                 if recvbuf is not None and source != MPI.PROC_NULL:
-                    self._data_in_region(HALO, d, i.flip())[:] = recvbuf
+                    self._data_in_region(HALO, d, i.flip())[:] = recvbuf.view(self.dtype)
 
         self._is_halo_dirty = False
 
diff --git a/devito/types/grid.py b/devito/types/grid.py
index 6683ab5713..f0d4a440d5 100644
--- a/devito/types/grid.py
+++ b/devito/types/grid.py
@@ -78,9 +78,10 @@ class Grid(CartesianDiscretization, ArgProvider):
     ----------
     shape : tuple of ints
         Shape of the computational domain in grid points.
-    extent : tuple of floats, default=unit box of extent 1m in all dimensions
+    extent : tuple of values interpretable as dtype, default=unit box of extent 1m
+    in all dimensions.
         Physical extent of the domain in m.
-    origin : tuple of floats, default=0.0 in all dimensions
+    origin : tuple of values interpretable as dtype, default=0.0 in all dimensions
         Physical coordinate of the origin of the domain.
     dimensions : tuple of SpaceDimension, optional
         The dimensions of the computational domain encapsulated by this Grid.
@@ -189,9 +190,12 @@ def __init__(self, shape, extent=None, origin=None, dimensions=None,
         self._distributor = Distributor(shape, dimensions, comm, self._topology)
 
         # The physical extent
-        self._extent = as_tuple(extent or tuple(1. for _ in self.shape))
+        extent = as_tuple(extent or tuple(1. for _ in self.shape))
+        self._extent = tuple(dtype(e) for e in extent)
 
-        self._origin = as_tuple(origin or tuple(0. for _ in self.shape))
+        # The origin of the grid
+        origin = as_tuple(origin or tuple(0. for _ in self.shape))
+        self._origin = tuple(dtype(o) for o in origin)
         self._origin_symbols = tuple(Scalar(name='o_%s' % d.name, dtype=dtype,
                                             is_const=True)
                                      for d in self.dimensions)
diff --git a/devito/types/object.py b/devito/types/object.py
index cba54b0add..032bca303a 100644
--- a/devito/types/object.py
+++ b/devito/types/object.py
@@ -54,6 +54,7 @@ def _sympystr(self, printer):
         return str(self)
 
     _ccode = _sympystr
+    _cxxcode = _sympystr
 
     def _hashable_content(self):
         return (self.name, self.dtype)
diff --git a/examples/compiler/03_iet-A.ipynb b/examples/compiler/03_iet-A.ipynb
index 8f8e26be34..3a40f70c86 100644
--- a/examples/compiler/03_iet-A.ipynb
+++ b/examples/compiler/03_iet-A.ipynb
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this example, `op` is represented as a `<Callable Kernel>`. Attached to it are metadata, such as `_headers` and `_includes`, as well as the `body`, which includes the children IET nodes. Here, the body is the concatenation of an `PointerCast` and a `List` object.\n"
+    "In this example, `op` is represented as a `<Callable Kernel>`. Attached to it are metadata, such as `headers` and `includes`, as well as the `body`, which includes the children IET nodes. Here, the body is the concatenation of an `PointerCast` and a `List` object.\n"
    ]
   },
   {
@@ -247,7 +247,7 @@
     }
    ],
    "source": [
-    "op._headers"
+    "op.headers"
    ]
   },
   {
@@ -267,7 +267,7 @@
     }
    ],
    "source": [
-    "op._includes"
+    "op.includes"
    ]
   },
   {
@@ -460,7 +460,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -474,7 +474,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/seismic/tutorials/17_fourier_mode.ipynb b/examples/seismic/tutorials/17_fourier_mode.ipynb
new file mode 100644
index 0000000000..c79edb329b
--- /dev/null
+++ b/examples/seismic/tutorials/17_fourier_mode.ipynb
@@ -0,0 +1,1519 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 17 - On-the-fly discrete Fourier transform\n",
+    "\n",
+    "In this notebook, we show how to compute Fourier modes during the propagation of the wavefield (on-the-fly) using the discrete Fourier transform (DFT). This method is known to be a memory efficient way to compute gradients for seismic inversion as only a few model-size Fourier modes are necessary instead of the full time history.\n",
+    "\n",
+    "The method illustrate the forward modeling to implement. the method in the following paper:\n",
+    "\n",
+    "- *Compressive least-squares migration with on-the-fly Fourier transforms*, Philipp A. Witte, Mathias Louboutin, Fabio Luporini, Gerard J. Gorman, and Felix J. Herrmann, 2019, Geophysics, 84(5), R655-R672. [DOI](https://doi.org/10.1190/geo2018-0490.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Theory\n",
+    "\n",
+    "The Fourier transform is a mathematical operation that transforms a function of time (or space) into a function of frequency. In the context of wave propagation, it allows us to analyze the frequency content of the wavefield.\n",
+    "\n",
+    "The discrete Fourier transform (DFT) is a specific kind of Fourier transform used for discrete data. It is defined as:\n",
+    "\n",
+    "$$ X_k = \\sum_{n=0}^{N-1} x_n e^{-i 2 \\pi k n / N} $$\n",
+    "\n",
+    "where:\n",
+    "-  $X_k$ is the DFT of the sequence $x_n$ \n",
+    "-  $N$ is the number of samples\n",
+    "-  $k$ is the frequency index\n",
+    "-  $n$ is the time index\n",
+    "\n",
+    "In seismic applications, the DFT can be used to compute the frequency components of the wavefield as it propagates through the subsurface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## On-the-fly Fourier Transform\n",
+    "\n",
+    "The on-the-fly Fourier transform is a technique where the Fourier transform is computed during the wavefield propagation, rather than storing the entire time history of the wavefield. This approach is memory efficient and particularly useful for large-scale seismic inversion problems.\n",
+    "\n",
+    "The key idea is to update the Fourier modes at each time step using the current wavefield values. This can be expressed as:\n",
+    "\n",
+    "$$ F_k(t+\\Delta t) = F_k(t) + u(t) e^{-i \\omega_k t \\Delta t} $$\n",
+    "\n",
+    "where:\n",
+    "-  $F_k(t)$ is the Fourier mode at frequency $\\omega_k$ and time $t$ \n",
+    "-  $u(t)$ is the wavefield at time $t$\n",
+    "-  $\\Delta t$ is the time step\n",
+    "\n",
+    "By updating the Fourier modes on-the-fly, we avoid the need to store the entire wavefield history, thus saving memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from devito import *\n",
+    "\n",
+    "from examples.seismic import demo_model, AcquisitionGeometry, plot_velocity\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "NUMA domain count autodetection failed, assuming 1\n",
+      "Operator `initdamp` ran in 0.01 s\n",
+      "Operator `initdamp` ran in 0.01 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "model = demo_model('layers-isotropic', vp=3.0, origin=(0., 0.), shape=(101, 101), spacing=(10., 10.), nbl=40, nlayers=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "plot_velocity(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "# Define acquisition geometry: source\n",
+    "\n",
+    "# First, position source centrally in all dimensions, then set depth\n",
+    "src_coordinates = np.empty((1, 2))\n",
+    "src_coordinates[0, :] = np.array(model.domain_size) * .5\n",
+    "src_coordinates[0, -1] = 20.  # Depth is 20m\n",
+    "\n",
+    "\n",
+    "# Define acquisition geometry: receivers\n",
+    "\n",
+    "# Initialize receivers for synthetic and imaging data\n",
+    "nreceivers = 101\n",
+    "rec_coordinates = np.empty((nreceivers, 2))\n",
+    "rec_coordinates[:, 0] = np.linspace(0, model.domain_size[0], num=nreceivers)\n",
+    "rec_coordinates[:, 1] = 30.\n",
+    "\n",
+    "# Geometry\n",
+    "t0 = 0.\n",
+    "tn = 500.  # Simulation last 1 second (1000 ms)\n",
+    "dt = model.critical_dt\n",
+    "geometry = AcquisitionGeometry(model, rec_coordinates, src_coordinates, t0, tn, f0=.010, src_type='Ricker')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle u(t + dt, x, y) = \\frac{- \\frac{- \\frac{2.0 u(t, x, y)}{dt^{2}} + \\frac{u(t - dt, x, y)}{dt^{2}}}{vp(x, y)} + \\frac{\\partial^{2}}{\\partial x^{2}} u(t, x, y) + \\frac{\\partial^{2}}{\\partial y^{2}} u(t, x, y) + \\frac{damp(x, y) u(t, x, y)}{dt}}{\\frac{damp(x, y)}{dt} + \\frac{1}{dt^{2} vp(x, y)}}$"
+      ],
+      "text/plain": [
+       "Eq(u(t + dt, x, y), (-(-2.0*u(t, x, y)/dt**2 + u(t - dt, x, y)/dt**2)/vp(x, y)**2 + Derivative(u(t, x, y), (x, 2)) + Derivative(u(t, x, y), (y, 2)) + damp(x, y)*u(t, x, y)/dt)/(damp(x, y)/dt + 1/(dt**2*vp(x, y)**2)))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the wavefield with the size of the model and the time dimension\n",
+    "u = TimeFunction(name=\"u\", grid=model.grid, time_order=2, space_order=2)\n",
+    "\n",
+    "# We can now write the PDE\n",
+    "pde = model.m * u.dt2 - u.laplace + model.damp * u.dt\n",
+    "\n",
+    "\n",
+    "# Stencil update\n",
+    "stencil = Eq(u.forward, solve(pde, u.forward))\n",
+    "stencil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freq_mode = Function(name='freq_modes', grid=model.grid, space_order=0, dtype=np.complex64)\n",
+    "freq = .01  # Compute 10Hz slice\n",
+    "omega = 2 * np.pi * freq\n",
+    "\n",
+    "basis = exp(-1j * omega * model.grid.time_dim * model.grid.time_dim.spacing)\n",
+    "dft = [Inc(freq_mode, basis * u)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Finally we define the source injection and receiver read function to generate the corresponding code\n",
+    "src = geometry.src\n",
+    "src_term = src.inject(field=u.forward, expr=src * dt**2 / model.m)\n",
+    "\n",
+    "# Create interpolation expression for receivers\n",
+    "rec = geometry.rec\n",
+    "rec_term = rec.interpolate(expr=u.forward)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "op = Operator([stencil] + src_term + rec_term + dft, subs=model.spacing_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       ".output_html .nv { color: #19177C } /* Name.Variable */\n",
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+       ".output_html .s1 { color: #BA2121 } /* Literal.String.Single */\n",
+       ".output_html .ss { color: #19177C } /* Literal.String.Symbol */\n",
+       ".output_html .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
+       ".output_html .fm { color: #00F } /* Name.Function.Magic */\n",
+       ".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
+       ".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
+       ".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
+       ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
+       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"cp\">#define _POSIX_C_SOURCE 200809L</span>\n",
+       "<span class=\"cp\">#define START(S) struct timeval start_ ## S , end_ ## S ; gettimeofday(&amp;start_ ## S , NULL);</span>\n",
+       "<span class=\"cp\">#define STOP(S,T) gettimeofday(&amp;end_ ## S, NULL); T-&gt;S += (double)(end_ ## S .tv_sec-start_ ## S.tv_sec)+(double)(end_ ## S .tv_usec-start_ ## S .tv_usec)/1000000;</span>\n",
+       "<span class=\"cp\">#define MAX(a,b) (((a) &gt; (b)) ? (a) : (b))</span>\n",
+       "\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;stdlib.h&quot;</span>\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;math.h&quot;</span>\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;sys/time.h&quot;</span>\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;omp.h&quot;</span>\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;complex.h&quot;</span>\n",
+       "\n",
+       "<span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span>\n",
+       "<span class=\"p\">{</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">unsigned</span><span class=\"w\"> </span><span class=\"kt\">long</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">size</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">unsigned</span><span class=\"w\"> </span><span class=\"kt\">long</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">npsize</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">unsigned</span><span class=\"w\"> </span><span class=\"kt\">long</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">dsize</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">hsize</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">hofs</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">oofs</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">dmap</span><span class=\"p\">;</span>\n",
+       "<span class=\"p\">}</span><span class=\"w\"> </span><span class=\"p\">;</span>\n",
+       "\n",
+       "<span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">profiler</span>\n",
+       "<span class=\"p\">{</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">section0</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">section1</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">section2</span><span class=\"p\">;</span>\n",
+       "<span class=\"p\">}</span><span class=\"w\"> </span><span class=\"p\">;</span>\n",
+       "\n",
+       "\n",
+       "<span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"nf\">Kernel</span><span class=\"p\">(</span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">damp_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">freq_modes_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">rec_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">rec_coords_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">src_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">src_coords_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">u_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">vp_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">dt</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">o_x</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">o_y</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_rec_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_rec_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_src_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_src_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">time_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">time_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">nthreads</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">nthreads_nonaffine</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">profiler</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "<span class=\"p\">{</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">damp</span><span class=\"p\">)[</span><span class=\"n\">damp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">damp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">damp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">freq_modes</span><span class=\"p\">)[</span><span class=\"n\">freq_modes_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">freq_modes_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">freq_modes_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">rec</span><span class=\"p\">)[</span><span class=\"n\">rec_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">rec_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">rec_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">rec_coords</span><span class=\"p\">)[</span><span class=\"n\">rec_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">rec_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">rec_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">src</span><span class=\"p\">)[</span><span class=\"n\">src_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">src_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">src_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">)[</span><span class=\"n\">src_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">src_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">src_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">u</span><span class=\"p\">)[</span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]][</span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]][</span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">vp</span><span class=\"p\">)[</span><span class=\"n\">vp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">vp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">vp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"n\">r2</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0F</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"n\">dt</span><span class=\"o\">*</span><span class=\"n\">dt</span><span class=\"p\">);</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"n\">r3</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0F</span><span class=\"o\">/</span><span class=\"n\">dt</span><span class=\"p\">;</span>\n",
+       "\n",
+       "<span class=\"w\">  </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">time_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">t0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">),</span><span class=\"w\"> </span><span class=\"n\">t1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">),</span><span class=\"w\"> </span><span class=\"n\">t2</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">);</span><span class=\"w\"> </span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">time_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">t0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">),</span><span class=\"w\"> </span><span class=\"n\">t1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">),</span><span class=\"w\"> </span><span class=\"n\">t2</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">))</span>\n",
+       "<span class=\"w\">  </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">    </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"n\">r1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">cexpf</span><span class=\"p\">(</span><span class=\"mf\">-6.28318530717959e-2F</span><span class=\"o\">*</span><span class=\"n\">time</span><span class=\"o\">*</span><span class=\"n\">_Complex_I</span><span class=\"o\">*</span><span class=\"n\">dt</span><span class=\"p\">);</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">START</span><span class=\"p\">(</span><span class=\"n\">section0</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">    </span><span class=\"cp\">#pragma omp parallel num_threads(nthreads)</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">      </span><span class=\"cp\">#pragma omp for schedule(dynamic,1)</span>\n",
+       "<span class=\"w\">      </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">        </span><span class=\"cp\">#pragma omp simd aligned(damp,freq_modes,u,vp:16)</span>\n",
+       "<span class=\"w\">        </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">          </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"n\">r4</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0F</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"n\">vp</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">vp</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">          </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t2</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">r4</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mf\">-2.0F</span><span class=\"o\">*</span><span class=\"n\">r2</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r2</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t1</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r3</span><span class=\"o\">*</span><span class=\"n\">damp</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">1.0e-2F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">3</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">3</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">3.99999991e-2F</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"n\">r4</span><span class=\"o\">*</span><span class=\"n\">r2</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r3</span><span class=\"o\">*</span><span class=\"n\">damp</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">          </span><span class=\"n\">freq_modes</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"n\">r1</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">];</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">STOP</span><span class=\"p\">(</span><span class=\"n\">section0</span><span class=\"p\">,</span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "\n",
+       "<span class=\"w\">    </span><span class=\"n\">START</span><span class=\"p\">(</span><span class=\"n\">section1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">    </span><span class=\"cp\">#pragma omp parallel num_threads(nthreads_nonaffine)</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">      </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">chunk_size</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)(</span><span class=\"n\">MAX</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)((</span><span class=\"mf\">1.0</span><span class=\"o\">/</span><span class=\"mf\">3.0</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">p_src_M</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">p_src_m</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">/</span><span class=\"n\">nthreads_nonaffine</span><span class=\"p\">)));</span>\n",
+       "<span class=\"w\">      </span><span class=\"cp\">#pragma omp for schedule(dynamic,chunk_size)</span>\n",
+       "<span class=\"w\">      </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_src</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">p_src_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">p_src</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">p_src_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">p_src</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">        </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">          </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">            </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)(</span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"mf\">1.0e-1</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">])));</span>\n",
+       "<span class=\"w\">            </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)(</span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"mf\">1.0e-1</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">])));</span>\n",
+       "<span class=\"w\">            </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">px</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">])</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]));</span>\n",
+       "<span class=\"w\">            </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">py</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">])</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">]));</span>\n",
+       "<span class=\"w\">            </span><span class=\"k\">if</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">&gt;=</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">&gt;=</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">              </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">3.06250F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">vp</span><span class=\"p\">[</span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">vp</span><span class=\"p\">[</span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">rsrcx</span><span class=\"o\">*</span><span class=\"n\">px</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">px</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">rsrcy</span><span class=\"o\">*</span><span class=\"n\">py</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">py</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"n\">src</span><span class=\"p\">[</span><span class=\"n\">time</span><span class=\"p\">][</span><span class=\"n\">p_src</span><span class=\"p\">];</span>\n",
+       "<span class=\"w\">              </span><span class=\"cp\">#pragma omp atomic update</span>\n",
+       "<span class=\"w\">              </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t2</span><span class=\"p\">][</span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"n\">r0</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">STOP</span><span class=\"p\">(</span><span class=\"n\">section1</span><span class=\"p\">,</span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "\n",
+       "<span class=\"w\">    </span><span class=\"n\">START</span><span class=\"p\">(</span><span class=\"n\">section2</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">    </span><span class=\"cp\">#pragma omp parallel num_threads(nthreads_nonaffine)</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">      </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">chunk_size</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)(</span><span class=\"n\">MAX</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)((</span><span class=\"mf\">1.0</span><span class=\"o\">/</span><span class=\"mf\">3.0</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">p_rec_M</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">p_rec_m</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">/</span><span class=\"n\">nthreads_nonaffine</span><span class=\"p\">)));</span>\n",
+       "<span class=\"w\">      </span><span class=\"cp\">#pragma omp for schedule(dynamic,chunk_size)</span>\n",
+       "<span class=\"w\">      </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_rec</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">p_rec_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">p_rec</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">p_rec_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">p_rec</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r7</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">rec_coords</span><span class=\"p\">[</span><span class=\"n\">p_rec</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r5</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"n\">r7</span><span class=\"p\">);</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)</span><span class=\"n\">r5</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r8</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">rec_coords</span><span class=\"p\">[</span><span class=\"n\">p_rec</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r6</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"n\">r8</span><span class=\"p\">);</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)</span><span class=\"n\">r6</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">px</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"n\">r5</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r7</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">py</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"n\">r6</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r8</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">sum</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">0.0F</span><span class=\"p\">;</span>\n",
+       "\n",
+       "<span class=\"w\">        </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">          </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">            </span><span class=\"k\">if</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">&gt;=</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">&gt;=</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">              </span><span class=\"n\">sum</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">rrecx</span><span class=\"o\">*</span><span class=\"n\">px</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">px</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">rrecy</span><span class=\"o\">*</span><span class=\"n\">py</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">py</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t2</span><span class=\"p\">][</span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">];</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">}</span>\n",
+       "\n",
+       "<span class=\"w\">        </span><span class=\"n\">rec</span><span class=\"p\">[</span><span class=\"n\">time</span><span class=\"p\">][</span><span class=\"n\">p_rec</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">sum</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">STOP</span><span class=\"p\">(</span><span class=\"n\">section2</span><span class=\"p\">,</span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">  </span><span class=\"p\">}</span>\n",
+       "\n",
+       "<span class=\"w\">  </span><span class=\"k\">return</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span>\n",
+       "<span class=\"p\">}</span>\n",
+       "</pre></div>\n"
+      ],
+      "text/latex": [
+       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define \\PYZus{}POSIX\\PYZus{}C\\PYZus{}SOURCE 200809L}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define START(S) struct timeval start\\PYZus{} \\PYZsh{}\\PYZsh{} S , end\\PYZus{} \\PYZsh{}\\PYZsh{} S ; gettimeofday(\\PYZam{}start\\PYZus{} \\PYZsh{}\\PYZsh{} S , NULL);}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define STOP(S,T) gettimeofday(\\PYZam{}end\\PYZus{} \\PYZsh{}\\PYZsh{} S, NULL); T\\PYZhy{}\\PYZgt{}S += (double)(end\\PYZus{} \\PYZsh{}\\PYZsh{} S .tv\\PYZus{}sec\\PYZhy{}start\\PYZus{} \\PYZsh{}\\PYZsh{} S.tv\\PYZus{}sec)+(double)(end\\PYZus{} \\PYZsh{}\\PYZsh{} S .tv\\PYZus{}usec\\PYZhy{}start\\PYZus{} \\PYZsh{}\\PYZsh{} S .tv\\PYZus{}usec)}\\PY{c+cp}{/}\\PY{c+cp}{1000000;}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define MAX(a,b) (((a) \\PYZgt{} (b)) ? (a) : (b))}\n",
+       "\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}stdlib.h\\PYZdq{}}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}math.h\\PYZdq{}}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}sys/time.h\\PYZdq{}}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}omp.h\\PYZdq{}}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}complex.h\\PYZdq{}}\n",
+       "\n",
+       "\\PY{k}{struct}\\PY{+w}{ }\\PY{n+nc}{dataobj}\n",
+       "\\PY{p}{\\PYZob{}}\n",
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+       "\\PY{+w}{  }\\PY{k+kt}{unsigned}\\PY{+w}{ }\\PY{k+kt}{long}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{npsize}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{unsigned}\\PY{+w}{ }\\PY{k+kt}{long}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{dsize}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{hsize}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{hofs}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{oofs}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{void}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{dmap}\\PY{p}{;}\n",
+       "\\PY{p}{\\PYZcb{}}\\PY{+w}{ }\\PY{p}{;}\n",
+       "\n",
+       "\\PY{k}{struct}\\PY{+w}{ }\\PY{n+nc}{profiler}\n",
+       "\\PY{p}{\\PYZob{}}\n",
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+       "\\PY{+w}{  }\\PY{k+kt}{double}\\PY{+w}{ }\\PY{n}{section2}\\PY{p}{;}\n",
+       "\\PY{p}{\\PYZcb{}}\\PY{+w}{ }\\PY{p}{;}\n",
+       "\n",
+       "\n",
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+       "\\PY{p}{\\PYZob{}}\n",
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+       "\n",
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+       "\n",
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+       "\\PY{+w}{  }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{    }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{k+kt}{\\PYZus{}Complex}\\PY{+w}{ }\\PY{n}{r1}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{cexpf}\\PY{p}{(}\\PY{l+m+mf}{\\PYZhy{}6.28318530717959e\\PYZhy{}2F}\\PY{o}{*}\\PY{n}{time}\\PY{o}{*}\\PY{n}{\\PYZus{}Complex\\PYZus{}I}\\PY{o}{*}\\PY{n}{dt}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{    }\\PY{n}{START}\\PY{p}{(}\\PY{n}{section0}\\PY{p}{)}\n",
+       "\\PY{+w}{    }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp parallel num\\PYZus{}threads(nthreads)}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{      }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp for schedule(dynamic,1)}\n",
+       "\\PY{+w}{      }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{x}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{x}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{x}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{        }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp simd aligned(damp,freq\\PYZus{}modes,u,vp:16)}\n",
+       "\\PY{+w}{        }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{y}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{y}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{y}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZob{}}\n",
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+       "\\PY{+w}{          }\\PY{n}{freq\\PYZus{}modes}\\PY{p}{[}\\PY{n}{x}\\PY{p}{]}\\PY{p}{[}\\PY{n}{y}\\PY{p}{]}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{r1}\\PY{o}{*}\\PY{n}{u}\\PY{p}{[}\\PY{n}{t0}\\PY{p}{]}\\PY{p}{[}\\PY{n}{x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{n}{STOP}\\PY{p}{(}\\PY{n}{section0}\\PY{p}{,}\\PY{n}{timers}\\PY{p}{)}\n",
+       "\n",
+       "\\PY{+w}{    }\\PY{n}{START}\\PY{p}{(}\\PY{n}{section1}\\PY{p}{)}\n",
+       "\\PY{+w}{    }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp parallel num\\PYZus{}threads(nthreads\\PYZus{}nonaffine)}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{      }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{chunk\\PYZus{}size}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{n}{MAX}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{p}{(}\\PY{l+m+mf}{1.0}\\PY{o}{/}\\PY{l+m+mf}{3.0}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{p\\PYZus{}src\\PYZus{}M}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src\\PYZus{}m}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{o}{/}\\PY{n}{nthreads\\PYZus{}nonaffine}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{      }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp for schedule(dynamic,chunk\\PYZus{}size)}\n",
+       "\\PY{+w}{      }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{        }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{0}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{          }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{0}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{            }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{n}{floorf}\\PY{p}{(}\\PY{l+m+mf}{1.0e\\PYZhy{}1}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{n}{floorf}\\PY{p}{(}\\PY{l+m+mf}{1.0e\\PYZhy{}1}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{px}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{)}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{floorf}\\PY{p}{(}\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{py}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{floorf}\\PY{p}{(}\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{k}{if}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{\\PYZgt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}m}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{\\PYZgt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}m}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}M}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}M}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{              }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r0}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{3.06250F}\\PY{o}{*}\\PY{p}{(}\\PY{n}{vp}\\PY{p}{[}\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{o}{*}\\PY{n}{vp}\\PY{p}{[}\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{rsrcx}\\PY{o}{*}\\PY{n}{px}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{rsrcx}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{px}\\PY{p}{)}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{rsrcy}\\PY{o}{*}\\PY{n}{py}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{py}\\PY{p}{)}\\PY{p}{)}\\PY{o}{*}\\PY{n}{src}\\PY{p}{[}\\PY{n}{time}\\PY{p}{]}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{;}\n",
+       "\\PY{+w}{              }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp atomic update}\n",
+       "\\PY{+w}{              }\\PY{n}{u}\\PY{p}{[}\\PY{n}{t2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{r0}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{n}{STOP}\\PY{p}{(}\\PY{n}{section1}\\PY{p}{,}\\PY{n}{timers}\\PY{p}{)}\n",
+       "\n",
+       "\\PY{+w}{    }\\PY{n}{START}\\PY{p}{(}\\PY{n}{section2}\\PY{p}{)}\n",
+       "\\PY{+w}{    }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp parallel num\\PYZus{}threads(nthreads\\PYZus{}nonaffine)}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{      }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{chunk\\PYZus{}size}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{n}{MAX}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{p}{(}\\PY{l+m+mf}{1.0}\\PY{o}{/}\\PY{l+m+mf}{3.0}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{p\\PYZus{}rec\\PYZus{}M}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec\\PYZus{}m}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{o}{/}\\PY{n}{nthreads\\PYZus{}nonaffine}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{      }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp for schedule(dynamic,chunk\\PYZus{}size)}\n",
+       "\\PY{+w}{      }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r7}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{rec\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}rec}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r5}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{floorf}\\PY{p}{(}\\PY{n}{r7}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{n}{r5}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r8}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{rec\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}rec}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r6}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{floorf}\\PY{p}{(}\\PY{n}{r8}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{n}{r6}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{px}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{n}{r5}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{r7}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{py}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{n}{r6}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{r8}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{sum}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{0.0F}\\PY{p}{;}\n",
+       "\n",
+       "\\PY{+w}{        }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{0}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{          }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{0}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{            }\\PY{k}{if}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{\\PYZgt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}m}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{\\PYZgt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}m}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}M}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}M}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{              }\\PY{n}{sum}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{rrecx}\\PY{o}{*}\\PY{n}{px}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{px}\\PY{p}{)}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{rrecy}\\PY{o}{*}\\PY{n}{py}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{py}\\PY{p}{)}\\PY{p}{)}\\PY{o}{*}\\PY{n}{u}\\PY{p}{[}\\PY{n}{t2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZcb{}}\n",
+       "\n",
+       "\\PY{+w}{        }\\PY{n}{rec}\\PY{p}{[}\\PY{n}{time}\\PY{p}{]}\\PY{p}{[}\\PY{n}{p\\PYZus{}rec}\\PY{p}{]}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{sum}\\PY{p}{;}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{n}{STOP}\\PY{p}{(}\\PY{n}{section2}\\PY{p}{,}\\PY{n}{timers}\\PY{p}{)}\n",
+       "\\PY{+w}{  }\\PY{p}{\\PYZcb{}}\n",
+       "\n",
+       "\\PY{+w}{  }\\PY{k}{return}\\PY{+w}{ }\\PY{l+m+mi}{0}\\PY{p}{;}\n",
+       "\\PY{p}{\\PYZcb{}}\n",
+       "\\end{Verbatim}\n"
+      ],
+      "text/plain": [
+       "#define _POSIX_C_SOURCE 200809L\n",
+       "#define START(S) struct timeval start_ ## S , end_ ## S ; gettimeofday(&start_ ## S , NULL);\n",
+       "#define STOP(S,T) gettimeofday(&end_ ## S, NULL); T->S += (double)(end_ ## S .tv_sec-start_ ## S.tv_sec)+(double)(end_ ## S .tv_usec-start_ ## S .tv_usec)/1000000;\n",
+       "#define MAX(a,b) (((a) > (b)) ? (a) : (b))\n",
+       "\n",
+       "#include \"stdlib.h\"\n",
+       "#include \"math.h\"\n",
+       "#include \"sys/time.h\"\n",
+       "#include \"omp.h\"\n",
+       "#include \"complex.h\"\n",
+       "\n",
+       "struct dataobj\n",
+       "{\n",
+       "  void *restrict data;\n",
+       "  unsigned long * size;\n",
+       "  unsigned long * npsize;\n",
+       "  unsigned long * dsize;\n",
+       "  int * hsize;\n",
+       "  int * hofs;\n",
+       "  int * oofs;\n",
+       "  void * dmap;\n",
+       "} ;\n",
+       "\n",
+       "struct profiler\n",
+       "{\n",
+       "  double section0;\n",
+       "  double section1;\n",
+       "  double section2;\n",
+       "} ;\n",
+       "\n",
+       "\n",
+       "int Kernel(struct dataobj *restrict damp_vec, struct dataobj *restrict freq_modes_vec, struct dataobj *restrict rec_vec, struct dataobj *restrict rec_coords_vec, struct dataobj *restrict src_vec, struct dataobj *restrict src_coords_vec, struct dataobj *restrict u_vec, struct dataobj *restrict vp_vec, const int x_M, const int x_m, const int y_M, const int y_m, const float dt, const float o_x, const float o_y, const int p_rec_M, const int p_rec_m, const int p_src_M, const int p_src_m, const int time_M, const int time_m, const int nthreads, const int nthreads_nonaffine, struct profiler * timers)\n",
+       "{\n",
+       "  float (*restrict damp)[damp_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[damp_vec->size[1]]) damp_vec->data;\n",
+       "  float _Complex (*restrict freq_modes)[freq_modes_vec->size[1]] __attribute__ ((aligned (64))) = (float _Complex (*)[freq_modes_vec->size[1]]) freq_modes_vec->data;\n",
+       "  float (*restrict rec)[rec_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[rec_vec->size[1]]) rec_vec->data;\n",
+       "  float (*restrict rec_coords)[rec_coords_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[rec_coords_vec->size[1]]) rec_coords_vec->data;\n",
+       "  float (*restrict src)[src_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[src_vec->size[1]]) src_vec->data;\n",
+       "  float (*restrict src_coords)[src_coords_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[src_coords_vec->size[1]]) src_coords_vec->data;\n",
+       "  float (*restrict u)[u_vec->size[1]][u_vec->size[2]] __attribute__ ((aligned (64))) = (float (*)[u_vec->size[1]][u_vec->size[2]]) u_vec->data;\n",
+       "  float (*restrict vp)[vp_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[vp_vec->size[1]]) vp_vec->data;\n",
+       "\n",
+       "  float _Complex r2 = 1.0F/(dt*dt);\n",
+       "  float _Complex r3 = 1.0F/dt;\n",
+       "\n",
+       "  for (int time = time_m, t0 = (time)%(3), t1 = (time + 2)%(3), t2 = (time + 1)%(3); time <= time_M; time += 1, t0 = (time)%(3), t1 = (time + 2)%(3), t2 = (time + 1)%(3))\n",
+       "  {\n",
+       "    float _Complex r1 = cexpf(-6.28318530717959e-2F*time*_Complex_I*dt);\n",
+       "    START(section0)\n",
+       "    #pragma omp parallel num_threads(nthreads)\n",
+       "    {\n",
+       "      #pragma omp for schedule(dynamic,1)\n",
+       "      for (int x = x_m; x <= x_M; x += 1)\n",
+       "      {\n",
+       "        #pragma omp simd aligned(damp,freq_modes,u,vp:16)\n",
+       "        for (int y = y_m; y <= y_M; y += 1)\n",
+       "        {\n",
+       "          float _Complex r4 = 1.0F/(vp[x + 2][y + 2]*vp[x + 2][y + 2]);\n",
+       "          u[t2][x + 2][y + 2] = (-r4*(-2.0F*r2*u[t0][x + 2][y + 2] + r2*u[t1][x + 2][y + 2]) + r3*damp[x + 2][y + 2]*u[t0][x + 2][y + 2] + 1.0e-2F*(u[t0][x + 1][y + 2] + u[t0][x + 2][y + 1] + u[t0][x + 2][y + 3] + u[t0][x + 3][y + 2]) - 3.99999991e-2F*u[t0][x + 2][y + 2])/(r4*r2 + r3*damp[x + 2][y + 2]);\n",
+       "          freq_modes[x][y] += r1*u[t0][x + 2][y + 2];\n",
+       "        }\n",
+       "      }\n",
+       "    }\n",
+       "    STOP(section0,timers)\n",
+       "\n",
+       "    START(section1)\n",
+       "    #pragma omp parallel num_threads(nthreads_nonaffine)\n",
+       "    {\n",
+       "      int chunk_size = (int)(MAX(1, (int)((1.0/3.0)*(p_src_M - p_src_m + 1)/nthreads_nonaffine)));\n",
+       "      #pragma omp for schedule(dynamic,chunk_size)\n",
+       "      for (int p_src = p_src_m; p_src <= p_src_M; p_src += 1)\n",
+       "      {\n",
+       "        for (int rsrcx = 0; rsrcx <= 1; rsrcx += 1)\n",
+       "        {\n",
+       "          for (int rsrcy = 0; rsrcy <= 1; rsrcy += 1)\n",
+       "          {\n",
+       "            int posx = (int)(floorf(1.0e-1*(-o_x + src_coords[p_src][0])));\n",
+       "            int posy = (int)(floorf(1.0e-1*(-o_y + src_coords[p_src][1])));\n",
+       "            float px = 1.0e-1F*(-o_x + src_coords[p_src][0]) - floorf(1.0e-1F*(-o_x + src_coords[p_src][0]));\n",
+       "            float py = 1.0e-1F*(-o_y + src_coords[p_src][1]) - floorf(1.0e-1F*(-o_y + src_coords[p_src][1]));\n",
+       "            if (rsrcx + posx >= x_m - 1 && rsrcy + posy >= y_m - 1 && rsrcx + posx <= x_M + 1 && rsrcy + posy <= y_M + 1)\n",
+       "            {\n",
+       "              float r0 = 3.06250F*(vp[posx + 2][posy + 2]*vp[posx + 2][posy + 2])*(rsrcx*px + (1 - rsrcx)*(1 - px))*(rsrcy*py + (1 - rsrcy)*(1 - py))*src[time][p_src];\n",
+       "              #pragma omp atomic update\n",
+       "              u[t2][rsrcx + posx + 2][rsrcy + posy + 2] += r0;\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "      }\n",
+       "    }\n",
+       "    STOP(section1,timers)\n",
+       "\n",
+       "    START(section2)\n",
+       "    #pragma omp parallel num_threads(nthreads_nonaffine)\n",
+       "    {\n",
+       "      int chunk_size = (int)(MAX(1, (int)((1.0/3.0)*(p_rec_M - p_rec_m + 1)/nthreads_nonaffine)));\n",
+       "      #pragma omp for schedule(dynamic,chunk_size)\n",
+       "      for (int p_rec = p_rec_m; p_rec <= p_rec_M; p_rec += 1)\n",
+       "      {\n",
+       "        float r7 = 1.0e-1F*(-o_x + rec_coords[p_rec][0]);\n",
+       "        float r5 = floorf(r7);\n",
+       "        int posx = (int)r5;\n",
+       "        float r8 = 1.0e-1F*(-o_y + rec_coords[p_rec][1]);\n",
+       "        float r6 = floorf(r8);\n",
+       "        int posy = (int)r6;\n",
+       "        float px = -r5 + r7;\n",
+       "        float py = -r6 + r8;\n",
+       "        float sum = 0.0F;\n",
+       "\n",
+       "        for (int rrecx = 0; rrecx <= 1; rrecx += 1)\n",
+       "        {\n",
+       "          for (int rrecy = 0; rrecy <= 1; rrecy += 1)\n",
+       "          {\n",
+       "            if (rrecx + posx >= x_m - 1 && rrecy + posy >= y_m - 1 && rrecx + posx <= x_M + 1 && rrecy + posy <= y_M + 1)\n",
+       "            {\n",
+       "              sum += (rrecx*px + (1 - rrecx)*(1 - px))*(rrecy*py + (1 - rrecy)*(1 - py))*u[t2][rrecx + posx + 2][rrecy + posy + 2];\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "\n",
+       "        rec[time][p_rec] = sum;\n",
+       "      }\n",
+       "    }\n",
+       "    STOP(section2,timers)\n",
+       "  }\n",
+       "\n",
+       "  return 0;\n",
+       "}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "Code(str(op.ccode), language='C')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.04 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
+       "                     PerfEntry(time=0.031197000000000023, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                    (PerfKey(name='section1', rank=None),\n",
+       "                     PerfEntry(time=0.0028049999999999933, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                    (PerfKey(name='section2', rank=None),\n",
+       "                     PerfEntry(time=0.0026769999999999967, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "op(dt=model.critical_dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(np.real(freq_mode.data.T), cmap='seismic', vmin=-1e2, vmax=1e2)\n",
+    "plt.colorbar()\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(np.imag(freq_mode.data.T), cmap='seismic', vmin=-1e2, vmax=1e2)\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert np.isclose(norm(freq_mode), 13873.049, atol=0, rtol=1e-4)\n",
+    "assert np.isclose(norm(u), 323.74207, atol=0, rtol=1e-4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple frequencies\n",
+    "\n",
+    "We can now extend the method to multiple frequencies. The idea is to compute the Fourier modes for a set of frequencies simultaneously. This can be done by adding a frequency dimension to the Fourier modes and updating them for each frequency at each time step.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nfreq = 5\n",
+    "f = Dimension(name='f')\n",
+    "\n",
+    "frequencies = Function(name='frequencies', dimensions=(f,), shape=(nfreq,), dtype=np.float32)\n",
+    "frequencies.data[:] = np.linspace(0.005, 0.015, num=nfreq)\n",
+    "\n",
+    "freq_modes = Function(name='freq_modes', grid=model.grid, space_order=0, dtype=np.complex64,\n",
+    "                      dimensions=(f, *model.grid.dimensions), shape=(nfreq, *model.grid.shape))\n",
+    "\n",
+    "omega = 2 * np.pi * frequencies\n",
+    "basis = exp(-1j * omega * model.grid.time_dim * model.grid.time_dim.spacing)\n",
+    "dfts = [Inc(freq_modes, basis * u)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "op = Operator([stencil] + src_term + rec_term + dfts, subs=model.spacing_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
+       "span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
+       ".output_html .hll { background-color: #ffffcc }\n",
+       ".output_html { background: #f8f8f8; }\n",
+       ".output_html .c { color: #3D7B7B; font-style: italic } /* Comment */\n",
+       ".output_html .err { border: 1px solid #F00 } /* Error */\n",
+       ".output_html .k { color: #008000; font-weight: bold } /* Keyword */\n",
+       ".output_html .o { color: #666 } /* Operator */\n",
+       ".output_html .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */\n",
+       ".output_html .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */\n",
+       ".output_html .cp { color: #9C6500 } /* Comment.Preproc */\n",
+       ".output_html .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */\n",
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+       ".output_html .nf { color: #00F } /* Name.Function */\n",
+       ".output_html .nl { color: #767600 } /* Name.Label */\n",
+       ".output_html .nn { color: #00F; font-weight: bold } /* Name.Namespace */\n",
+       ".output_html .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
+       ".output_html .nv { color: #19177C } /* Name.Variable */\n",
+       ".output_html .ow { color: #A2F; font-weight: bold } /* Operator.Word */\n",
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+       ".output_html .mf { color: #666 } /* Literal.Number.Float */\n",
+       ".output_html .mh { color: #666 } /* Literal.Number.Hex */\n",
+       ".output_html .mi { color: #666 } /* Literal.Number.Integer */\n",
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+       ".output_html .sa { color: #BA2121 } /* Literal.String.Affix */\n",
+       ".output_html .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
+       ".output_html .sc { color: #BA2121 } /* Literal.String.Char */\n",
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+       ".output_html .sx { color: #008000 } /* Literal.String.Other */\n",
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+       ".output_html .s1 { color: #BA2121 } /* Literal.String.Single */\n",
+       ".output_html .ss { color: #19177C } /* Literal.String.Symbol */\n",
+       ".output_html .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
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+       ".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
+       ".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
+       ".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
+       ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
+       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"cp\">#define _POSIX_C_SOURCE 200809L</span>\n",
+       "<span class=\"cp\">#define START(S) struct timeval start_ ## S , end_ ## S ; gettimeofday(&amp;start_ ## S , NULL);</span>\n",
+       "<span class=\"cp\">#define STOP(S,T) gettimeofday(&amp;end_ ## S, NULL); T-&gt;S += (double)(end_ ## S .tv_sec-start_ ## S.tv_sec)+(double)(end_ ## S .tv_usec-start_ ## S .tv_usec)/1000000;</span>\n",
+       "<span class=\"cp\">#define MAX(a,b) (((a) &gt; (b)) ? (a) : (b))</span>\n",
+       "<span class=\"cp\">#define MIN(a,b) (((a) &lt; (b)) ? (a) : (b))</span>\n",
+       "\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;stdlib.h&quot;</span>\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;math.h&quot;</span>\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;sys/time.h&quot;</span>\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;omp.h&quot;</span>\n",
+       "<span class=\"cp\">#include</span><span class=\"w\"> </span><span class=\"cpf\">&quot;complex.h&quot;</span>\n",
+       "\n",
+       "<span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span>\n",
+       "<span class=\"p\">{</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">unsigned</span><span class=\"w\"> </span><span class=\"kt\">long</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">size</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">unsigned</span><span class=\"w\"> </span><span class=\"kt\">long</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">npsize</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">unsigned</span><span class=\"w\"> </span><span class=\"kt\">long</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">dsize</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">hsize</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">hofs</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">oofs</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">dmap</span><span class=\"p\">;</span>\n",
+       "<span class=\"p\">}</span><span class=\"w\"> </span><span class=\"p\">;</span>\n",
+       "\n",
+       "<span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">profiler</span>\n",
+       "<span class=\"p\">{</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">section0</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">section1</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">section2</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">section3</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">section4</span><span class=\"p\">;</span>\n",
+       "<span class=\"p\">}</span><span class=\"w\"> </span><span class=\"p\">;</span>\n",
+       "\n",
+       "\n",
+       "<span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"nf\">Kernel</span><span class=\"p\">(</span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">damp_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">freq_modes_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">frequencies_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">rec_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">rec_coords_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">src_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">src_coords_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">u_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">dataobj</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">vp_vec</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">dt</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">f0_blk0_size</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">f_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">f_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">o_x</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">o_y</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_rec_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_rec_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_src_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_src_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">time_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">time_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x0_blk0_size</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">nthreads</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">nthreads_nonaffine</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">f_size</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">struct</span><span class=\"w\"> </span><span class=\"nc\">profiler</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "<span class=\"p\">{</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">r1_vec</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)));</span>\n",
+       "<span class=\"w\">  </span><span class=\"n\">posix_memalign</span><span class=\"p\">((</span><span class=\"kt\">void</span><span class=\"o\">**</span><span class=\"p\">)(</span><span class=\"o\">&amp;</span><span class=\"n\">r1_vec</span><span class=\"p\">),</span><span class=\"mi\">64</span><span class=\"p\">,</span><span class=\"n\">f_size</span><span class=\"o\">*</span><span class=\"k\">sizeof</span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"p\">));</span>\n",
+       "\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">damp</span><span class=\"p\">)[</span><span class=\"n\">damp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">damp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">damp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">freq_modes</span><span class=\"p\">)[</span><span class=\"n\">freq_modes_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]][</span><span class=\"n\">freq_modes_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">freq_modes_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]][</span><span class=\"n\">freq_modes_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">freq_modes_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">frequencies</span><span class=\"p\">)</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">))</span><span class=\"w\"> </span><span class=\"n\">frequencies_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">r1</span><span class=\"p\">)</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"kt\">_Complex</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">))</span><span class=\"w\"> </span><span class=\"n\">r1_vec</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">rec</span><span class=\"p\">)[</span><span class=\"n\">rec_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">rec_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">rec_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">rec_coords</span><span class=\"p\">)[</span><span class=\"n\">rec_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">rec_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">rec_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">src</span><span class=\"p\">)[</span><span class=\"n\">src_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">src_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">src_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">)[</span><span class=\"n\">src_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">src_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">src_coords_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">u</span><span class=\"p\">)[</span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]][</span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]][</span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">u_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"kr\">restrict</span><span class=\"w\"> </span><span class=\"n\">vp</span><span class=\"p\">)[</span><span class=\"n\">vp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span><span class=\"w\"> </span><span class=\"n\">__attribute__</span><span class=\"w\"> </span><span class=\"p\">((</span><span class=\"n\">aligned</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">64</span><span class=\"p\">)))</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)[</span><span class=\"n\">vp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">size</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]])</span><span class=\"w\"> </span><span class=\"n\">vp_vec</span><span class=\"o\">-&gt;</span><span class=\"n\">data</span><span class=\"p\">;</span>\n",
+       "\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r2</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0F</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"n\">dt</span><span class=\"o\">*</span><span class=\"n\">dt</span><span class=\"p\">);</span>\n",
+       "<span class=\"w\">  </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r3</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0F</span><span class=\"o\">/</span><span class=\"n\">dt</span><span class=\"p\">;</span>\n",
+       "\n",
+       "<span class=\"w\">  </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">time_m</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">t0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">),</span><span class=\"w\"> </span><span class=\"n\">t1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">),</span><span class=\"w\"> </span><span class=\"n\">t2</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">);</span><span class=\"w\"> </span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">time_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">t0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">),</span><span class=\"w\"> </span><span class=\"n\">t1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">),</span><span class=\"w\"> </span><span class=\"n\">t2</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">time</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">%</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">))</span>\n",
+       "<span class=\"w\">  </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">START</span><span class=\"p\">(</span><span class=\"n\">section0</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">    </span><span class=\"cp\">#pragma omp parallel num_threads(nthreads)</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">      </span><span class=\"cp\">#pragma omp for schedule(dynamic,1)</span>\n",
+       "<span class=\"w\">      </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">        </span><span class=\"cp\">#pragma omp simd aligned(damp,u,vp:16)</span>\n",
+       "<span class=\"w\">        </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">          </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r4</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0F</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"n\">vp</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">vp</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">          </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t2</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">r4</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mf\">-2.0F</span><span class=\"o\">*</span><span class=\"n\">r2</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r2</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t1</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r3</span><span class=\"o\">*</span><span class=\"n\">damp</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">1.0e-2F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">3</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">3</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">3.99999991e-2F</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"n\">r4</span><span class=\"o\">*</span><span class=\"n\">r2</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r3</span><span class=\"o\">*</span><span class=\"n\">damp</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">STOP</span><span class=\"p\">(</span><span class=\"n\">section0</span><span class=\"p\">,</span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "\n",
+       "<span class=\"w\">    </span><span class=\"n\">START</span><span class=\"p\">(</span><span class=\"n\">section1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">    </span><span class=\"cp\">#pragma omp parallel num_threads(nthreads_nonaffine)</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">      </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">chunk_size</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)(</span><span class=\"n\">MAX</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)((</span><span class=\"mf\">1.0</span><span class=\"o\">/</span><span class=\"mf\">3.0</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">p_src_M</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">p_src_m</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">/</span><span class=\"n\">nthreads_nonaffine</span><span class=\"p\">)));</span>\n",
+       "<span class=\"w\">      </span><span class=\"cp\">#pragma omp for schedule(dynamic,chunk_size)</span>\n",
+       "<span class=\"w\">      </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_src</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">p_src_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">p_src</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">p_src_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">p_src</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">        </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">          </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">            </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)(</span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"mf\">1.0e-1</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">])));</span>\n",
+       "<span class=\"w\">            </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)(</span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"mf\">1.0e-1</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">])));</span>\n",
+       "<span class=\"w\">            </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">px</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">])</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]));</span>\n",
+       "<span class=\"w\">            </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">py</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">])</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">src_coords</span><span class=\"p\">[</span><span class=\"n\">p_src</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">]));</span>\n",
+       "<span class=\"w\">            </span><span class=\"k\">if</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">&gt;=</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">&gt;=</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">              </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">3.06250F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">vp</span><span class=\"p\">[</span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">vp</span><span class=\"p\">[</span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">rsrcx</span><span class=\"o\">*</span><span class=\"n\">px</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">rsrcx</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">px</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">rsrcy</span><span class=\"o\">*</span><span class=\"n\">py</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">rsrcy</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">py</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"n\">src</span><span class=\"p\">[</span><span class=\"n\">time</span><span class=\"p\">][</span><span class=\"n\">p_src</span><span class=\"p\">];</span>\n",
+       "<span class=\"w\">              </span><span class=\"cp\">#pragma omp atomic update</span>\n",
+       "<span class=\"w\">              </span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t2</span><span class=\"p\">][</span><span class=\"n\">rsrcx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">rsrcy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"n\">r0</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">STOP</span><span class=\"p\">(</span><span class=\"n\">section1</span><span class=\"p\">,</span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "\n",
+       "<span class=\"w\">    </span><span class=\"n\">START</span><span class=\"p\">(</span><span class=\"n\">section2</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">    </span><span class=\"cp\">#pragma omp parallel num_threads(nthreads_nonaffine)</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">      </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">chunk_size</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)(</span><span class=\"n\">MAX</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)((</span><span class=\"mf\">1.0</span><span class=\"o\">/</span><span class=\"mf\">3.0</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">p_rec_M</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">p_rec_m</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">/</span><span class=\"n\">nthreads_nonaffine</span><span class=\"p\">)));</span>\n",
+       "<span class=\"w\">      </span><span class=\"cp\">#pragma omp for schedule(dynamic,chunk_size)</span>\n",
+       "<span class=\"w\">      </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">p_rec</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">p_rec_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">p_rec</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">p_rec_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">p_rec</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r7</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">rec_coords</span><span class=\"p\">[</span><span class=\"n\">p_rec</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r5</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"n\">r7</span><span class=\"p\">);</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)</span><span class=\"n\">r5</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r8</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0e-1F</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">o_y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">rec_coords</span><span class=\"p\">[</span><span class=\"n\">p_rec</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">r6</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">floorf</span><span class=\"p\">(</span><span class=\"n\">r8</span><span class=\"p\">);</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"p\">)</span><span class=\"n\">r6</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">px</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"n\">r5</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r7</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">py</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"n\">r6</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">r8</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">        </span><span class=\"kt\">float</span><span class=\"w\"> </span><span class=\"n\">sum</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">0.0F</span><span class=\"p\">;</span>\n",
+       "\n",
+       "<span class=\"w\">        </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">          </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">            </span><span class=\"k\">if</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">&gt;=</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">&gt;=</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">&amp;&amp;</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">              </span><span class=\"n\">sum</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">rrecx</span><span class=\"o\">*</span><span class=\"n\">px</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">rrecx</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">px</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">rrecy</span><span class=\"o\">*</span><span class=\"n\">py</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">rrecy</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">py</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t2</span><span class=\"p\">][</span><span class=\"n\">rrecx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posx</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">rrecy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">posy</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">];</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">}</span>\n",
+       "\n",
+       "<span class=\"w\">        </span><span class=\"n\">rec</span><span class=\"p\">[</span><span class=\"n\">time</span><span class=\"p\">][</span><span class=\"n\">p_rec</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">sum</span><span class=\"p\">;</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">STOP</span><span class=\"p\">(</span><span class=\"n\">section2</span><span class=\"p\">,</span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "\n",
+       "<span class=\"w\">    </span><span class=\"n\">START</span><span class=\"p\">(</span><span class=\"n\">section3</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">    </span><span class=\"cp\">#pragma omp parallel num_threads(nthreads)</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">      </span><span class=\"cp\">#pragma omp for schedule(static,1)</span>\n",
+       "<span class=\"w\">      </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">f</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">f_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">f</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">f_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">f</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">        </span><span class=\"n\">r1</span><span class=\"p\">[</span><span class=\"n\">f</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">cexpf</span><span class=\"p\">(</span><span class=\"mf\">-6.283185307179590F</span><span class=\"o\">*</span><span class=\"n\">time</span><span class=\"o\">*</span><span class=\"n\">_Complex_I</span><span class=\"o\">*</span><span class=\"n\">dt</span><span class=\"o\">*</span><span class=\"n\">frequencies</span><span class=\"p\">[</span><span class=\"n\">f</span><span class=\"p\">]);</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">STOP</span><span class=\"p\">(</span><span class=\"n\">section3</span><span class=\"p\">,</span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "\n",
+       "<span class=\"w\">    </span><span class=\"n\">START</span><span class=\"p\">(</span><span class=\"n\">section4</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">    </span><span class=\"cp\">#pragma omp parallel num_threads(nthreads)</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">      </span><span class=\"cp\">#pragma omp for collapse(2) schedule(static,1)</span>\n",
+       "<span class=\"w\">      </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">f0_blk0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">f_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">f0_blk0</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">f_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">f0_blk0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"n\">f0_blk0_size</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">        </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x0_blk0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">x_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">x0_blk0</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">x_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">x0_blk0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"n\">x0_blk0_size</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">          </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">f</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">f0_blk0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">f</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">MIN</span><span class=\"p\">(</span><span class=\"n\">f_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">f0_blk0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">f0_blk0_size</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">);</span><span class=\"w\"> </span><span class=\"n\">f</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">            </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">x0_blk0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">MIN</span><span class=\"p\">(</span><span class=\"n\">x_M</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">x0_blk0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">x0_blk0_size</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">);</span><span class=\"w\"> </span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">              </span><span class=\"cp\">#pragma omp simd aligned(freq_modes,u:16)</span>\n",
+       "<span class=\"w\">              </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">y_m</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">&lt;=</span><span class=\"w\"> </span><span class=\"n\">y_M</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">              </span><span class=\"p\">{</span>\n",
+       "<span class=\"w\">                </span><span class=\"n\">freq_modes</span><span class=\"p\">[</span><span class=\"n\">f</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"n\">r1</span><span class=\"p\">[</span><span class=\"n\">f</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">u</span><span class=\"p\">[</span><span class=\"n\">t0</span><span class=\"p\">][</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">][</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">];</span>\n",
+       "<span class=\"w\">              </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">            </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">          </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">        </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">      </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"p\">}</span>\n",
+       "<span class=\"w\">    </span><span class=\"n\">STOP</span><span class=\"p\">(</span><span class=\"n\">section4</span><span class=\"p\">,</span><span class=\"n\">timers</span><span class=\"p\">)</span>\n",
+       "<span class=\"w\">  </span><span class=\"p\">}</span>\n",
+       "\n",
+       "<span class=\"w\">  </span><span class=\"n\">free</span><span class=\"p\">(</span><span class=\"n\">r1_vec</span><span class=\"p\">);</span>\n",
+       "\n",
+       "<span class=\"w\">  </span><span class=\"k\">return</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span>\n",
+       "<span class=\"p\">}</span>\n",
+       "</pre></div>\n"
+      ],
+      "text/latex": [
+       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define \\PYZus{}POSIX\\PYZus{}C\\PYZus{}SOURCE 200809L}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define START(S) struct timeval start\\PYZus{} \\PYZsh{}\\PYZsh{} S , end\\PYZus{} \\PYZsh{}\\PYZsh{} S ; gettimeofday(\\PYZam{}start\\PYZus{} \\PYZsh{}\\PYZsh{} S , NULL);}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define STOP(S,T) gettimeofday(\\PYZam{}end\\PYZus{} \\PYZsh{}\\PYZsh{} S, NULL); T\\PYZhy{}\\PYZgt{}S += (double)(end\\PYZus{} \\PYZsh{}\\PYZsh{} S .tv\\PYZus{}sec\\PYZhy{}start\\PYZus{} \\PYZsh{}\\PYZsh{} S.tv\\PYZus{}sec)+(double)(end\\PYZus{} \\PYZsh{}\\PYZsh{} S .tv\\PYZus{}usec\\PYZhy{}start\\PYZus{} \\PYZsh{}\\PYZsh{} S .tv\\PYZus{}usec)}\\PY{c+cp}{/}\\PY{c+cp}{1000000;}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define MAX(a,b) (((a) \\PYZgt{} (b)) ? (a) : (b))}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{define MIN(a,b) (((a) \\PYZlt{} (b)) ? (a) : (b))}\n",
+       "\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}stdlib.h\\PYZdq{}}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}math.h\\PYZdq{}}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}sys/time.h\\PYZdq{}}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}omp.h\\PYZdq{}}\n",
+       "\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{include}\\PY{+w}{ }\\PY{c+cpf}{\\PYZdq{}complex.h\\PYZdq{}}\n",
+       "\n",
+       "\\PY{k}{struct}\\PY{+w}{ }\\PY{n+nc}{dataobj}\n",
+       "\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{void}\\PY{+w}{ }\\PY{o}{*}\\PY{k+kr}{restrict}\\PY{+w}{ }\\PY{n}{data}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{unsigned}\\PY{+w}{ }\\PY{k+kt}{long}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{size}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{unsigned}\\PY{+w}{ }\\PY{k+kt}{long}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{npsize}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{unsigned}\\PY{+w}{ }\\PY{k+kt}{long}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{dsize}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{hsize}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{hofs}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{oofs}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{void}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{n}{dmap}\\PY{p}{;}\n",
+       "\\PY{p}{\\PYZcb{}}\\PY{+w}{ }\\PY{p}{;}\n",
+       "\n",
+       "\\PY{k}{struct}\\PY{+w}{ }\\PY{n+nc}{profiler}\n",
+       "\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{double}\\PY{+w}{ }\\PY{n}{section0}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{double}\\PY{+w}{ }\\PY{n}{section1}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{double}\\PY{+w}{ }\\PY{n}{section2}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{double}\\PY{+w}{ }\\PY{n}{section3}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{double}\\PY{+w}{ }\\PY{n}{section4}\\PY{p}{;}\n",
+       "\\PY{p}{\\PYZcb{}}\\PY{+w}{ }\\PY{p}{;}\n",
+       "\n",
+       "\n",
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+       "\\PY{p}{\\PYZob{}}\n",
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+       "\n",
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+       "\\PY{+w}{  }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{k+kt}{\\PYZus{}Complex}\\PY{+w}{ }\\PY{p}{(}\\PY{o}{*}\\PY{k+kr}{restrict}\\PY{+w}{ }\\PY{n}{r1}\\PY{p}{)}\\PY{+w}{ }\\PY{n}{\\PYZus{}\\PYZus{}attribute\\PYZus{}\\PYZus{}}\\PY{+w}{ }\\PY{p}{(}\\PY{p}{(}\\PY{n}{aligned}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{64}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{float}\\PY{+w}{ }\\PY{k+kt}{\\PYZus{}Complex}\\PY{+w}{ }\\PY{p}{(}\\PY{o}{*}\\PY{p}{)}\\PY{p}{)}\\PY{+w}{ }\\PY{n}{r1\\PYZus{}vec}\\PY{p}{;}\n",
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+       "\\PY{+w}{  }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{p}{(}\\PY{o}{*}\\PY{k+kr}{restrict}\\PY{+w}{ }\\PY{n}{src}\\PY{p}{)}\\PY{p}{[}\\PY{n}{src\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{size}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{]}\\PY{+w}{ }\\PY{n}{\\PYZus{}\\PYZus{}attribute\\PYZus{}\\PYZus{}}\\PY{+w}{ }\\PY{p}{(}\\PY{p}{(}\\PY{n}{aligned}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{64}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{float}\\PY{+w}{ }\\PY{p}{(}\\PY{o}{*}\\PY{p}{)}\\PY{p}{[}\\PY{n}{src\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{size}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{]}\\PY{p}{)}\\PY{+w}{ }\\PY{n}{src\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{data}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{p}{(}\\PY{o}{*}\\PY{k+kr}{restrict}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{)}\\PY{p}{[}\\PY{n}{src\\PYZus{}coords\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{size}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{]}\\PY{+w}{ }\\PY{n}{\\PYZus{}\\PYZus{}attribute\\PYZus{}\\PYZus{}}\\PY{+w}{ }\\PY{p}{(}\\PY{p}{(}\\PY{n}{aligned}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{64}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{float}\\PY{+w}{ }\\PY{p}{(}\\PY{o}{*}\\PY{p}{)}\\PY{p}{[}\\PY{n}{src\\PYZus{}coords\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{size}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{]}\\PY{p}{)}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{data}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{p}{(}\\PY{o}{*}\\PY{k+kr}{restrict}\\PY{+w}{ }\\PY{n}{u}\\PY{p}{)}\\PY{p}{[}\\PY{n}{u\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{size}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{]}\\PY{p}{[}\\PY{n}{u\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{size}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{]}\\PY{+w}{ }\\PY{n}{\\PYZus{}\\PYZus{}attribute\\PYZus{}\\PYZus{}}\\PY{+w}{ }\\PY{p}{(}\\PY{p}{(}\\PY{n}{aligned}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{64}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{float}\\PY{+w}{ }\\PY{p}{(}\\PY{o}{*}\\PY{p}{)}\\PY{p}{[}\\PY{n}{u\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{size}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{]}\\PY{p}{[}\\PY{n}{u\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{size}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{]}\\PY{p}{)}\\PY{+w}{ }\\PY{n}{u\\PYZus{}vec}\\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}}\\PY{n}{data}\\PY{p}{;}\n",
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+       "\n",
+       "\\PY{+w}{  }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r2}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0F}\\PY{o}{/}\\PY{p}{(}\\PY{n}{dt}\\PY{o}{*}\\PY{n}{dt}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{  }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r3}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0F}\\PY{o}{/}\\PY{n}{dt}\\PY{p}{;}\n",
+       "\n",
+       "\\PY{+w}{  }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{time}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{time\\PYZus{}m}\\PY{p}{,}\\PY{+w}{ }\\PY{n}{t0}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{time}\\PY{p}{)}\\PY{o}{\\PYZpc{}}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{,}\\PY{+w}{ }\\PY{n}{t1}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{time}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{)}\\PY{o}{\\PYZpc{}}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{,}\\PY{+w}{ }\\PY{n}{t2}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{time}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{o}{\\PYZpc{}}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{time}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{time\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{time}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{,}\\PY{+w}{ }\\PY{n}{t0}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{time}\\PY{p}{)}\\PY{o}{\\PYZpc{}}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{,}\\PY{+w}{ }\\PY{n}{t1}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{time}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{)}\\PY{o}{\\PYZpc{}}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{,}\\PY{+w}{ }\\PY{n}{t2}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{time}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{o}{\\PYZpc{}}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{)}\n",
+       "\\PY{+w}{  }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{    }\\PY{n}{START}\\PY{p}{(}\\PY{n}{section0}\\PY{p}{)}\n",
+       "\\PY{+w}{    }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp parallel num\\PYZus{}threads(nthreads)}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{      }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp for schedule(dynamic,1)}\n",
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+       "\\PY{+w}{      }\\PY{p}{\\PYZob{}}\n",
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+       "\\PY{+w}{        }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{n}{STOP}\\PY{p}{(}\\PY{n}{section0}\\PY{p}{,}\\PY{n}{timers}\\PY{p}{)}\n",
+       "\n",
+       "\\PY{+w}{    }\\PY{n}{START}\\PY{p}{(}\\PY{n}{section1}\\PY{p}{)}\n",
+       "\\PY{+w}{    }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp parallel num\\PYZus{}threads(nthreads\\PYZus{}nonaffine)}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{      }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{chunk\\PYZus{}size}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{n}{MAX}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{p}{(}\\PY{l+m+mf}{1.0}\\PY{o}{/}\\PY{l+m+mf}{3.0}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{p\\PYZus{}src\\PYZus{}M}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src\\PYZus{}m}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{o}{/}\\PY{n}{nthreads\\PYZus{}nonaffine}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{      }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp for schedule(dynamic,chunk\\PYZus{}size)}\n",
+       "\\PY{+w}{      }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{p\\PYZus{}src}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZob{}}\n",
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+       "\\PY{+w}{        }\\PY{p}{\\PYZob{}}\n",
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+       "\\PY{+w}{          }\\PY{p}{\\PYZob{}}\n",
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+       "\\PY{+w}{            }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{n}{floorf}\\PY{p}{(}\\PY{l+m+mf}{1.0e\\PYZhy{}1}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{px}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{)}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{floorf}\\PY{p}{(}\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{py}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{floorf}\\PY{p}{(}\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{src\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{k}{if}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{\\PYZgt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}m}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{\\PYZgt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}m}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}M}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}M}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{              }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r0}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{3.06250F}\\PY{o}{*}\\PY{p}{(}\\PY{n}{vp}\\PY{p}{[}\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{o}{*}\\PY{n}{vp}\\PY{p}{[}\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{rsrcx}\\PY{o}{*}\\PY{n}{px}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{rsrcx}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{px}\\PY{p}{)}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{rsrcy}\\PY{o}{*}\\PY{n}{py}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{rsrcy}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{py}\\PY{p}{)}\\PY{p}{)}\\PY{o}{*}\\PY{n}{src}\\PY{p}{[}\\PY{n}{time}\\PY{p}{]}\\PY{p}{[}\\PY{n}{p\\PYZus{}src}\\PY{p}{]}\\PY{p}{;}\n",
+       "\\PY{+w}{              }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp atomic update}\n",
+       "\\PY{+w}{              }\\PY{n}{u}\\PY{p}{[}\\PY{n}{t2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{rsrcx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{rsrcy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{r0}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{n}{STOP}\\PY{p}{(}\\PY{n}{section1}\\PY{p}{,}\\PY{n}{timers}\\PY{p}{)}\n",
+       "\n",
+       "\\PY{+w}{    }\\PY{n}{START}\\PY{p}{(}\\PY{n}{section2}\\PY{p}{)}\n",
+       "\\PY{+w}{    }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp parallel num\\PYZus{}threads(nthreads\\PYZus{}nonaffine)}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{      }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{chunk\\PYZus{}size}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{n}{MAX}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{p}{(}\\PY{p}{(}\\PY{l+m+mf}{1.0}\\PY{o}{/}\\PY{l+m+mf}{3.0}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{p\\PYZus{}rec\\PYZus{}M}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec\\PYZus{}m}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{o}{/}\\PY{n}{nthreads\\PYZus{}nonaffine}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{      }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp for schedule(dynamic,chunk\\PYZus{}size)}\n",
+       "\\PY{+w}{      }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{p\\PYZus{}rec}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r7}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{rec\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}rec}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r5}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{floorf}\\PY{p}{(}\\PY{n}{r7}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{n}{r5}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r8}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{1.0e\\PYZhy{}1F}\\PY{o}{*}\\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{n}{o\\PYZus{}y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{rec\\PYZus{}coords}\\PY{p}{[}\\PY{n}{p\\PYZus{}rec}\\PY{p}{]}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{r6}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{floorf}\\PY{p}{(}\\PY{n}{r8}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{p}{)}\\PY{n}{r6}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{px}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{n}{r5}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{r7}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{py}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{n}{r6}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{r8}\\PY{p}{;}\n",
+       "\\PY{+w}{        }\\PY{k+kt}{float}\\PY{+w}{ }\\PY{n}{sum}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mf}{0.0F}\\PY{p}{;}\n",
+       "\n",
+       "\\PY{+w}{        }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{0}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{          }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{0}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{            }\\PY{k}{if}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{\\PYZgt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}m}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{\\PYZgt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}m}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}M}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZam{}}\\PY{o}{\\PYZam{}}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}M}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{              }\\PY{n}{sum}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{p}{(}\\PY{n}{rrecx}\\PY{o}{*}\\PY{n}{px}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{rrecx}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{px}\\PY{p}{)}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{n}{rrecy}\\PY{o}{*}\\PY{n}{py}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{rrecy}\\PY{p}{)}\\PY{o}{*}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{n}{py}\\PY{p}{)}\\PY{p}{)}\\PY{o}{*}\\PY{n}{u}\\PY{p}{[}\\PY{n}{t2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{rrecx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posx}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{rrecy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{posy}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{;}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZcb{}}\n",
+       "\n",
+       "\\PY{+w}{        }\\PY{n}{rec}\\PY{p}{[}\\PY{n}{time}\\PY{p}{]}\\PY{p}{[}\\PY{n}{p\\PYZus{}rec}\\PY{p}{]}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{sum}\\PY{p}{;}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{n}{STOP}\\PY{p}{(}\\PY{n}{section2}\\PY{p}{,}\\PY{n}{timers}\\PY{p}{)}\n",
+       "\n",
+       "\\PY{+w}{    }\\PY{n}{START}\\PY{p}{(}\\PY{n}{section3}\\PY{p}{)}\n",
+       "\\PY{+w}{    }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp parallel num\\PYZus{}threads(nthreads)}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{      }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp for schedule(static,1)}\n",
+       "\\PY{+w}{      }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{f}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{f\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{f}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{f\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{f}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{        }\\PY{n}{r1}\\PY{p}{[}\\PY{n}{f}\\PY{p}{]}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{cexpf}\\PY{p}{(}\\PY{l+m+mf}{\\PYZhy{}6.283185307179590F}\\PY{o}{*}\\PY{n}{time}\\PY{o}{*}\\PY{n}{\\PYZus{}Complex\\PYZus{}I}\\PY{o}{*}\\PY{n}{dt}\\PY{o}{*}\\PY{n}{frequencies}\\PY{p}{[}\\PY{n}{f}\\PY{p}{]}\\PY{p}{)}\\PY{p}{;}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{n}{STOP}\\PY{p}{(}\\PY{n}{section3}\\PY{p}{,}\\PY{n}{timers}\\PY{p}{)}\n",
+       "\n",
+       "\\PY{+w}{    }\\PY{n}{START}\\PY{p}{(}\\PY{n}{section4}\\PY{p}{)}\n",
+       "\\PY{+w}{    }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp parallel num\\PYZus{}threads(nthreads)}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{      }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp for collapse(2) schedule(static,1)}\n",
+       "\\PY{+w}{      }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{f0\\PYZus{}blk0}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{f\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{f0\\PYZus{}blk0}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{f\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{f0\\PYZus{}blk0}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{f0\\PYZus{}blk0\\PYZus{}size}\\PY{p}{)}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{        }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{x0\\PYZus{}blk0}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{x0\\PYZus{}blk0}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{x0\\PYZus{}blk0}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x0\\PYZus{}blk0\\PYZus{}size}\\PY{p}{)}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{          }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{f}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{f0\\PYZus{}blk0}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{f}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{MIN}\\PY{p}{(}\\PY{n}{f\\PYZus{}M}\\PY{p}{,}\\PY{+w}{ }\\PY{n}{f0\\PYZus{}blk0}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{f0\\PYZus{}blk0\\PYZus{}size}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{f}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{            }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{x}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{x0\\PYZus{}blk0}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{x}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{MIN}\\PY{p}{(}\\PY{n}{x\\PYZus{}M}\\PY{p}{,}\\PY{+w}{ }\\PY{n}{x0\\PYZus{}blk0}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{x0\\PYZus{}blk0\\PYZus{}size}\\PY{+w}{ }\\PY{o}{\\PYZhy{}}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{x}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{              }\\PY{c+cp}{\\PYZsh{}}\\PY{c+cp}{pragma omp simd aligned(freq\\PYZus{}modes,u:16)}\n",
+       "\\PY{+w}{              }\\PY{k}{for}\\PY{+w}{ }\\PY{p}{(}\\PY{k+kt}{int}\\PY{+w}{ }\\PY{n}{y}\\PY{+w}{ }\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}m}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{y}\\PY{+w}{ }\\PY{o}{\\PYZlt{}}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{y\\PYZus{}M}\\PY{p}{;}\\PY{+w}{ }\\PY{n}{y}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{l+m+mi}{1}\\PY{p}{)}\n",
+       "\\PY{+w}{              }\\PY{p}{\\PYZob{}}\n",
+       "\\PY{+w}{                }\\PY{n}{freq\\PYZus{}modes}\\PY{p}{[}\\PY{n}{f}\\PY{p}{]}\\PY{p}{[}\\PY{n}{x}\\PY{p}{]}\\PY{p}{[}\\PY{n}{y}\\PY{p}{]}\\PY{+w}{ }\\PY{o}{+}\\PY{o}{=}\\PY{+w}{ }\\PY{n}{r1}\\PY{p}{[}\\PY{n}{f}\\PY{p}{]}\\PY{o}{*}\\PY{n}{u}\\PY{p}{[}\\PY{n}{t0}\\PY{p}{]}\\PY{p}{[}\\PY{n}{x}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{[}\\PY{n}{y}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{l+m+mi}{2}\\PY{p}{]}\\PY{p}{;}\n",
+       "\\PY{+w}{              }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{            }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{          }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{        }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{      }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{p}{\\PYZcb{}}\n",
+       "\\PY{+w}{    }\\PY{n}{STOP}\\PY{p}{(}\\PY{n}{section4}\\PY{p}{,}\\PY{n}{timers}\\PY{p}{)}\n",
+       "\\PY{+w}{  }\\PY{p}{\\PYZcb{}}\n",
+       "\n",
+       "\\PY{+w}{  }\\PY{n}{free}\\PY{p}{(}\\PY{n}{r1\\PYZus{}vec}\\PY{p}{)}\\PY{p}{;}\n",
+       "\n",
+       "\\PY{+w}{  }\\PY{k}{return}\\PY{+w}{ }\\PY{l+m+mi}{0}\\PY{p}{;}\n",
+       "\\PY{p}{\\PYZcb{}}\n",
+       "\\end{Verbatim}\n"
+      ],
+      "text/plain": [
+       "#define _POSIX_C_SOURCE 200809L\n",
+       "#define START(S) struct timeval start_ ## S , end_ ## S ; gettimeofday(&start_ ## S , NULL);\n",
+       "#define STOP(S,T) gettimeofday(&end_ ## S, NULL); T->S += (double)(end_ ## S .tv_sec-start_ ## S.tv_sec)+(double)(end_ ## S .tv_usec-start_ ## S .tv_usec)/1000000;\n",
+       "#define MAX(a,b) (((a) > (b)) ? (a) : (b))\n",
+       "#define MIN(a,b) (((a) < (b)) ? (a) : (b))\n",
+       "\n",
+       "#include \"stdlib.h\"\n",
+       "#include \"math.h\"\n",
+       "#include \"sys/time.h\"\n",
+       "#include \"omp.h\"\n",
+       "#include \"complex.h\"\n",
+       "\n",
+       "struct dataobj\n",
+       "{\n",
+       "  void *restrict data;\n",
+       "  unsigned long * size;\n",
+       "  unsigned long * npsize;\n",
+       "  unsigned long * dsize;\n",
+       "  int * hsize;\n",
+       "  int * hofs;\n",
+       "  int * oofs;\n",
+       "  void * dmap;\n",
+       "} ;\n",
+       "\n",
+       "struct profiler\n",
+       "{\n",
+       "  double section0;\n",
+       "  double section1;\n",
+       "  double section2;\n",
+       "  double section3;\n",
+       "  double section4;\n",
+       "} ;\n",
+       "\n",
+       "\n",
+       "int Kernel(struct dataobj *restrict damp_vec, struct dataobj *restrict freq_modes_vec, struct dataobj *restrict frequencies_vec, struct dataobj *restrict rec_vec, struct dataobj *restrict rec_coords_vec, struct dataobj *restrict src_vec, struct dataobj *restrict src_coords_vec, struct dataobj *restrict u_vec, struct dataobj *restrict vp_vec, const int x_M, const int x_m, const int y_M, const int y_m, const float dt, const int f0_blk0_size, const int f_M, const int f_m, const float o_x, const float o_y, const int p_rec_M, const int p_rec_m, const int p_src_M, const int p_src_m, const int time_M, const int time_m, const int x0_blk0_size, const int nthreads, const int nthreads_nonaffine, const int f_size, struct profiler * timers)\n",
+       "{\n",
+       "  float _Complex *restrict r1_vec __attribute__ ((aligned (64)));\n",
+       "  posix_memalign((void**)(&r1_vec),64,f_size*sizeof(float _Complex));\n",
+       "\n",
+       "  float (*restrict damp)[damp_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[damp_vec->size[1]]) damp_vec->data;\n",
+       "  float _Complex (*restrict freq_modes)[freq_modes_vec->size[1]][freq_modes_vec->size[2]] __attribute__ ((aligned (64))) = (float _Complex (*)[freq_modes_vec->size[1]][freq_modes_vec->size[2]]) freq_modes_vec->data;\n",
+       "  float (*restrict frequencies) __attribute__ ((aligned (64))) = (float (*)) frequencies_vec->data;\n",
+       "  float _Complex (*restrict r1) __attribute__ ((aligned (64))) = (float _Complex (*)) r1_vec;\n",
+       "  float (*restrict rec)[rec_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[rec_vec->size[1]]) rec_vec->data;\n",
+       "  float (*restrict rec_coords)[rec_coords_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[rec_coords_vec->size[1]]) rec_coords_vec->data;\n",
+       "  float (*restrict src)[src_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[src_vec->size[1]]) src_vec->data;\n",
+       "  float (*restrict src_coords)[src_coords_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[src_coords_vec->size[1]]) src_coords_vec->data;\n",
+       "  float (*restrict u)[u_vec->size[1]][u_vec->size[2]] __attribute__ ((aligned (64))) = (float (*)[u_vec->size[1]][u_vec->size[2]]) u_vec->data;\n",
+       "  float (*restrict vp)[vp_vec->size[1]] __attribute__ ((aligned (64))) = (float (*)[vp_vec->size[1]]) vp_vec->data;\n",
+       "\n",
+       "  float r2 = 1.0F/(dt*dt);\n",
+       "  float r3 = 1.0F/dt;\n",
+       "\n",
+       "  for (int time = time_m, t0 = (time)%(3), t1 = (time + 2)%(3), t2 = (time + 1)%(3); time <= time_M; time += 1, t0 = (time)%(3), t1 = (time + 2)%(3), t2 = (time + 1)%(3))\n",
+       "  {\n",
+       "    START(section0)\n",
+       "    #pragma omp parallel num_threads(nthreads)\n",
+       "    {\n",
+       "      #pragma omp for schedule(dynamic,1)\n",
+       "      for (int x = x_m; x <= x_M; x += 1)\n",
+       "      {\n",
+       "        #pragma omp simd aligned(damp,u,vp:16)\n",
+       "        for (int y = y_m; y <= y_M; y += 1)\n",
+       "        {\n",
+       "          float r4 = 1.0F/(vp[x + 2][y + 2]*vp[x + 2][y + 2]);\n",
+       "          u[t2][x + 2][y + 2] = (-r4*(-2.0F*r2*u[t0][x + 2][y + 2] + r2*u[t1][x + 2][y + 2]) + r3*damp[x + 2][y + 2]*u[t0][x + 2][y + 2] + 1.0e-2F*(u[t0][x + 1][y + 2] + u[t0][x + 2][y + 1] + u[t0][x + 2][y + 3] + u[t0][x + 3][y + 2]) - 3.99999991e-2F*u[t0][x + 2][y + 2])/(r4*r2 + r3*damp[x + 2][y + 2]);\n",
+       "        }\n",
+       "      }\n",
+       "    }\n",
+       "    STOP(section0,timers)\n",
+       "\n",
+       "    START(section1)\n",
+       "    #pragma omp parallel num_threads(nthreads_nonaffine)\n",
+       "    {\n",
+       "      int chunk_size = (int)(MAX(1, (int)((1.0/3.0)*(p_src_M - p_src_m + 1)/nthreads_nonaffine)));\n",
+       "      #pragma omp for schedule(dynamic,chunk_size)\n",
+       "      for (int p_src = p_src_m; p_src <= p_src_M; p_src += 1)\n",
+       "      {\n",
+       "        for (int rsrcx = 0; rsrcx <= 1; rsrcx += 1)\n",
+       "        {\n",
+       "          for (int rsrcy = 0; rsrcy <= 1; rsrcy += 1)\n",
+       "          {\n",
+       "            int posx = (int)(floorf(1.0e-1*(-o_x + src_coords[p_src][0])));\n",
+       "            int posy = (int)(floorf(1.0e-1*(-o_y + src_coords[p_src][1])));\n",
+       "            float px = 1.0e-1F*(-o_x + src_coords[p_src][0]) - floorf(1.0e-1F*(-o_x + src_coords[p_src][0]));\n",
+       "            float py = 1.0e-1F*(-o_y + src_coords[p_src][1]) - floorf(1.0e-1F*(-o_y + src_coords[p_src][1]));\n",
+       "            if (rsrcx + posx >= x_m - 1 && rsrcy + posy >= y_m - 1 && rsrcx + posx <= x_M + 1 && rsrcy + posy <= y_M + 1)\n",
+       "            {\n",
+       "              float r0 = 3.06250F*(vp[posx + 2][posy + 2]*vp[posx + 2][posy + 2])*(rsrcx*px + (1 - rsrcx)*(1 - px))*(rsrcy*py + (1 - rsrcy)*(1 - py))*src[time][p_src];\n",
+       "              #pragma omp atomic update\n",
+       "              u[t2][rsrcx + posx + 2][rsrcy + posy + 2] += r0;\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "      }\n",
+       "    }\n",
+       "    STOP(section1,timers)\n",
+       "\n",
+       "    START(section2)\n",
+       "    #pragma omp parallel num_threads(nthreads_nonaffine)\n",
+       "    {\n",
+       "      int chunk_size = (int)(MAX(1, (int)((1.0/3.0)*(p_rec_M - p_rec_m + 1)/nthreads_nonaffine)));\n",
+       "      #pragma omp for schedule(dynamic,chunk_size)\n",
+       "      for (int p_rec = p_rec_m; p_rec <= p_rec_M; p_rec += 1)\n",
+       "      {\n",
+       "        float r7 = 1.0e-1F*(-o_x + rec_coords[p_rec][0]);\n",
+       "        float r5 = floorf(r7);\n",
+       "        int posx = (int)r5;\n",
+       "        float r8 = 1.0e-1F*(-o_y + rec_coords[p_rec][1]);\n",
+       "        float r6 = floorf(r8);\n",
+       "        int posy = (int)r6;\n",
+       "        float px = -r5 + r7;\n",
+       "        float py = -r6 + r8;\n",
+       "        float sum = 0.0F;\n",
+       "\n",
+       "        for (int rrecx = 0; rrecx <= 1; rrecx += 1)\n",
+       "        {\n",
+       "          for (int rrecy = 0; rrecy <= 1; rrecy += 1)\n",
+       "          {\n",
+       "            if (rrecx + posx >= x_m - 1 && rrecy + posy >= y_m - 1 && rrecx + posx <= x_M + 1 && rrecy + posy <= y_M + 1)\n",
+       "            {\n",
+       "              sum += (rrecx*px + (1 - rrecx)*(1 - px))*(rrecy*py + (1 - rrecy)*(1 - py))*u[t2][rrecx + posx + 2][rrecy + posy + 2];\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "\n",
+       "        rec[time][p_rec] = sum;\n",
+       "      }\n",
+       "    }\n",
+       "    STOP(section2,timers)\n",
+       "\n",
+       "    START(section3)\n",
+       "    #pragma omp parallel num_threads(nthreads)\n",
+       "    {\n",
+       "      #pragma omp for schedule(static,1)\n",
+       "      for (int f = f_m; f <= f_M; f += 1)\n",
+       "      {\n",
+       "        r1[f] = cexpf(-6.283185307179590F*time*_Complex_I*dt*frequencies[f]);\n",
+       "      }\n",
+       "    }\n",
+       "    STOP(section3,timers)\n",
+       "\n",
+       "    START(section4)\n",
+       "    #pragma omp parallel num_threads(nthreads)\n",
+       "    {\n",
+       "      #pragma omp for collapse(2) schedule(static,1)\n",
+       "      for (int f0_blk0 = f_m; f0_blk0 <= f_M; f0_blk0 += f0_blk0_size)\n",
+       "      {\n",
+       "        for (int x0_blk0 = x_m; x0_blk0 <= x_M; x0_blk0 += x0_blk0_size)\n",
+       "        {\n",
+       "          for (int f = f0_blk0; f <= MIN(f_M, f0_blk0 + f0_blk0_size - 1); f += 1)\n",
+       "          {\n",
+       "            for (int x = x0_blk0; x <= MIN(x_M, x0_blk0 + x0_blk0_size - 1); x += 1)\n",
+       "            {\n",
+       "              #pragma omp simd aligned(freq_modes,u:16)\n",
+       "              for (int y = y_m; y <= y_M; y += 1)\n",
+       "              {\n",
+       "                freq_modes[f][x][y] += r1[f]*u[t0][x + 2][y + 2];\n",
+       "              }\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "      }\n",
+       "    }\n",
+       "    STOP(section4,timers)\n",
+       "  }\n",
+       "\n",
+       "  free(r1_vec);\n",
+       "\n",
+       "  return 0;\n",
+       "}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "Code(str(op.ccode), language='C')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.05 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
+       "                     PerfEntry(time=0.007022999999999993, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                    (PerfKey(name='section1', rank=None),\n",
+       "                     PerfEntry(time=0.002476999999999992, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                    (PerfKey(name='section2', rank=None),\n",
+       "                     PerfEntry(time=0.002686000000000001, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                    (PerfKey(name='section3', rank=None),\n",
+       "                     PerfEntry(time=0.0023849999999999943, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                    (PerfKey(name='section4', rank=None),\n",
+       "                     PerfEntry(time=0.025902999999999992, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "u.data.fill(0)\n",
+    "op(dt=model.critical_dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x3000 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "plt.figure(figsize=(12, 30))\n",
+    "for i in range(5):\n",
+    "    plt.subplot(5, 2, 2*i+1)\n",
+    "    plt.imshow(np.real(freq_modes.data[i].T), cmap='seismic', vmin=-1e2, vmax=1e2)\n",
+    "    plt.title(f'Real part {1e3*frequencies.data[i]} Hz')\n",
+    "    plt.subplot(5, 2, 2*i+2)\n",
+    "    plt.imshow(np.imag(freq_modes.data[i].T), cmap='seismic', vmin=-1e2, vmax=1e2)\n",
+    "    plt.title(f'Imaginary part {1e3*frequencies.data[i]} Hz')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert np.isclose(norm(freq_modes), 26016.113, atol=0, rtol=1e-4)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/userapi/06_sparse_operations.ipynb b/examples/userapi/06_sparse_operations.ipynb
index 26359cf6e1..b4c131e247 100644
--- a/examples/userapi/06_sparse_operations.ipynb
+++ b/examples/userapi/06_sparse_operations.ipynb
@@ -277,8 +277,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eq(posx, (int)(floor((-o_x + s_coords(p_s, 0))/h_x)))\n",
-      "Eq(posy, (int)(floor((-o_y + s_coords(p_s, 1))/h_y)))\n",
+      "Eq(posx, (int)floor((-o_x + s_coords(p_s, 0))/h_x))\n",
+      "Eq(posy, (int)floor((-o_y + s_coords(p_s, 1))/h_y))\n",
       "Eq(px, -floor((-o_x + s_coords(p_s, 0))/h_x) + (-o_x + s_coords(p_s, 0))/h_x)\n",
       "Eq(py, -floor((-o_y + s_coords(p_s, 1))/h_y) + (-o_y + s_coords(p_s, 1))/h_y)\n",
       "Eq(sum, 0.0)\n",
@@ -484,8 +484,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eq(posx, (int)(floor((-o_x + s_coords(p_s, 0))/h_x)))\n",
-      "Eq(posy, (int)(floor((-o_y + s_coords(p_s, 1))/h_y)))\n",
+      "Eq(posx, (int)floor((-o_x + s_coords(p_s, 0))/h_x))\n",
+      "Eq(posy, (int)floor((-o_y + s_coords(p_s, 1))/h_y))\n",
       "Eq(sum, 0.0)\n",
       "Inc(sum, wsincrsx(p_s, rsx + 3)*wsincrsy(p_s, rsy + 3)*f(t, rsx + posx, rsy + posy))\n",
       "Eq(s(time, p_s), sum)\n"
diff --git a/tests/test_builtins.py b/tests/test_builtins.py
index 483bd6a135..8df6c4ad2c 100644
--- a/tests/test_builtins.py
+++ b/tests/test_builtins.py
@@ -155,7 +155,7 @@ def test_gs_1d_float(self, sigma):
     def test_gs_2d_int(self, sigma):
         """Test the Gaussian smoother in 2d."""
 
-        a = ascent()
+        a = ascent().astype(np.int32)
         sp_smoothed = gaussian_filter(a, sigma=sigma)
         dv_smoothed = gaussian_smooth(a, sigma=sigma)
 
@@ -169,8 +169,7 @@ def test_gs_2d_int(self, sigma):
     def test_gs_2d_float(self, sigma):
         """Test the Gaussian smoother in 2d."""
 
-        a = ascent()
-        a = a+0.1
+        a = ascent()+0.1
         sp_smoothed = gaussian_filter(a, sigma=sigma)
         dv_smoothed = gaussian_smooth(a, sigma=sigma)
 
@@ -375,6 +374,23 @@ def test_inner_sparse(self):
         term2 = np.inner(rec0.data.reshape(-1), rec1.data.reshape(-1))
         assert np.isclose(term1/term2 - 1, 0.0, rtol=0.0, atol=1e-5)
 
+    @pytest.mark.parametrize('dtype', [np.float32, np.complex64])
+    def test_norm_dense(self, dtype):
+        """
+        Test that norm produces the correct result against NumPy
+        """
+        grid = Grid((101, 101), extent=(1000., 1000.))
+
+        f = Function(name='f', grid=grid, dtype=dtype)
+
+        f.data[:] = 1 + np.random.randn(*f.shape).astype(grid.dtype)
+        if np.iscomplexobj(f.data):
+            f.data[:] += 1j*np.random.randn(*f.shape).astype(grid.dtype)
+        term1 = np.linalg.norm(f.data)
+        term2 = norm(f)
+        assert np.isreal(term2)
+        assert np.isclose(term1/term2 - 1, 0.0, rtol=0.0, atol=1e-5)
+
     def test_norm_sparse(self):
         """
         Test that norm produces the correct result against NumPy
diff --git a/tests/test_caching.py b/tests/test_caching.py
index 47d90680d9..26ce3b6721 100644
--- a/tests/test_caching.py
+++ b/tests/test_caching.py
@@ -453,8 +453,16 @@ def test_grid_objs(self):
         assert y0 is y1
         assert x0.spacing is x1.spacing
         assert y0.spacing is y1.spacing
-        assert ox0 is ox1
-        assert oy0 is oy1
+
+    def test_grid_dtypes(self):
+        """
+        Test that two grids with different dtypes have different hash values.
+        """
+
+        grid0 = Grid(shape=(4, 4), dtype=np.float32)
+        grid1 = Grid(shape=(4, 4), dtype=np.float64)
+
+        assert hash(grid0) != hash(grid1)
 
     def test_special_symbols(self):
         """
diff --git a/tests/test_differentiable.py b/tests/test_differentiable.py
index ef73579ef5..cfc254c3b9 100644
--- a/tests/test_differentiable.py
+++ b/tests/test_differentiable.py
@@ -17,14 +17,17 @@ def test_differentiable():
     assert isinstance(e * a, Mul)
     assert isinstance(a * a, Pow)
     assert isinstance(1 / (a * a), Pow)
+    assert (a + e*a).dtype == a.dtype
 
     addition = a + 1.2 * a.dx
     assert isinstance(addition, Add)
     assert all(isinstance(a, Differentiable) for a in addition.args)
+    assert addition.dtype == a.dtype
 
     addition2 = a + e * a.dx
     assert isinstance(addition2, Add)
     assert all(isinstance(a, Differentiable) for a in addition2.args)
+    assert addition2.dtype == a.dtype
 
 
 def test_diffify():
diff --git a/tests/test_dtypes.py b/tests/test_dtypes.py
new file mode 100644
index 0000000000..fad1840383
--- /dev/null
+++ b/tests/test_dtypes.py
@@ -0,0 +1,273 @@
+import numpy as np
+import pytest
+import sympy
+
+from devito import Constant, Eq, Function, Grid, Operator, exp, log, sin
+from devito.ir.cgen.printer import BasePrinter
+from devito.passes.iet.langbase import LangBB
+from devito.passes.iet.languages.C import CBB, CPrinter
+from devito.passes.iet.languages.openacc import AccBB, AccPrinter
+from devito.passes.iet.languages.openmp import OmpBB
+from devito.symbolics.extended_dtypes import ctypes_vector_mapper
+from devito.types.basic import Basic, Scalar, Symbol
+from devito.types.dense import TimeFunction
+
+# Mappers for language-specific types and headers
+_languages: dict[str, type[LangBB]] = {
+    'C': CBB,
+    'openmp': OmpBB,
+    'openacc': AccBB
+}
+
+_printers: dict[str, type[BasePrinter]] = {
+    'C': CPrinter,
+    'openmp': CPrinter,
+    'openacc': AccPrinter
+}
+
+
+def _get_language(language: str, **_) -> type[LangBB]:
+    """
+    Gets the language building block type from parametrized kwargs.
+    """
+    return _languages[language]
+
+
+def _get_printer(language: str, **_) -> type[BasePrinter]:
+    """
+    Gets the printer building block type from parametrized kwargs.
+    """
+    return _printers[language]
+
+
+def _config_kwargs(platform: str, language: str) -> dict[str, str]:
+    """
+    Generates kwargs for Operator to test language-specific behavior.
+    """
+    return {
+        'platform': platform,
+        'language': language,
+    }
+
+
+# List of pararmetrized operator kwargs for testing language-specific behavior
+_configs: list[dict[str, str]] = [
+    _config_kwargs(*cfg) for cfg in [
+        ('cpu64', 'C'),
+        ('cpu64', 'openmp'),
+        ('nvidiaX', 'openacc')
+    ]
+]
+
+
+def kw_id(kwargs):
+    # For more readable log
+    return "-".join(f'{k}' for k in kwargs.values())
+
+
+@pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+@pytest.mark.parametrize('kwargs', _configs, ids=kw_id)
+def test_dtype_mapping(dtype: np.dtype[np.inexact], kwargs: dict[str, str],
+                       expected=None) -> None:
+    """
+    Tests that half and complex floats' dtypes result in the correct type
+    strings in generated code.
+    """
+    # Set up an operator
+    grid = Grid(shape=(3, 3))
+    x, y = grid.dimensions
+
+    c = Constant(name='c', dtype=dtype)
+    u = Function(name='u', grid=grid, dtype=dtype)
+    eq = Eq(u, c * x * y)
+    op = Operator(eq, **kwargs)
+
+    # Check ctypes of the mapped parameters
+    params: dict[str, Basic] = {p.name: p for p in op.parameters}
+    _u, _c = params['u'], params['c']
+    assert type(_u.indexed._C_ctype._type_()) == ctypes_vector_mapper[dtype]
+    assert _c._C_ctype == expected or ctypes_vector_mapper[dtype]
+
+
+@pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+@pytest.mark.parametrize('kwargs', _configs, ids=kw_id)
+def test_cse_ctypes(dtype: np.dtype[np.inexact], kwargs: dict[str, str]) -> None:
+    """
+    Tests that variables introduced by CSE have the correct type strings in
+    the generated code.
+    """
+    # Retrieve the language-specific type mapping
+    printer: type[BasePrinter] = _get_printer(**kwargs)
+
+    # Set up an operator
+    grid = Grid(shape=(3, 3))
+    x, y = grid.dimensions
+
+    c = Constant(name='c', dtype=dtype)
+    u = Function(name='u', grid=grid, dtype=dtype)
+    # sin(c) should be CSE'd
+    eq = Eq(u, x * x.spacing + y * y.spacing * sympy.sin(c))
+    op = Operator(eq, **kwargs)
+
+    # Ensure the CSE'd variable has the correct type
+    assert f'{printer()._print(ctypes_vector_mapper[dtype])} r0' in str(op)
+
+
+@pytest.mark.parametrize('dtype', [np.float32, np.complex64, np.complex128])
+@pytest.mark.parametrize('kwargs', _configs, ids=kw_id)
+def test_complex_headers(dtype: np.dtype[np.inexact], kwargs: dict[str, str]) -> None:
+    np.dtype
+    """
+    Tests that the correct complex headers are included when complex dtypes
+    are present in the operator, and omitted otherwise.
+    """
+    # Set up an operator
+    grid = Grid(shape=(3, 3))
+    x, y = grid.dimensions
+
+    c = Constant(name='c', dtype=dtype)
+    u = Function(name='u', grid=grid, dtype=dtype)
+    eq = Eq(u, c * x * y)
+    op = Operator(eq, **kwargs)
+
+    # Check that the complex header is included <=> complex dtypes are present
+    header: str = _get_language(**kwargs).get('includes-complex')
+    if np.issubdtype(dtype, np.complexfloating):
+        assert header in op._includes
+    else:
+        assert header not in op._includes
+
+
+@pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+@pytest.mark.parametrize('kwargs', _configs, ids=kw_id)
+def test_imag_unit(dtype: np.complexfloating, kwargs: dict[str, str]) -> None:
+    """
+    Tests that the correct literal is used for the imaginary unit.
+    """
+    # Determine the expected imaginary unit string
+    unit_str: str
+    if kwargs['platform'] == 'cpu64':
+        # In C we multiply by the _Complex_I macro constant
+        unit_str = '_Complex_I'
+    else:
+        # C++ provides imaginary literals
+        if dtype == np.complex64:
+            unit_str = '1if'
+        else:
+            unit_str = '1i'
+
+    # Set up an operator
+    s = Symbol(name='s', dtype=dtype)
+    eq = Eq(s, 2.0 + 3.0j)
+    op = Operator(eq, **kwargs)
+
+    # Check that the correct imaginary unit is used
+    assert unit_str in str(op)
+
+
+@pytest.mark.parametrize('dtype', [np.float32, np.float64,
+                                   np.complex64, np.complex128])
+@pytest.mark.parametrize(['sym', 'fun'], [(exp, np.exp),
+                                          (log, np.log),
+                                          (sin, np.sin)])
+def test_math_functions(dtype: np.dtype[np.inexact],
+                        sym: sympy.Function, fun: np.ufunc) -> None:
+    """
+    Tests that the correct math functions are used, and their results cast
+    and assigned appropriately for different float precisions and for
+    complex floats/doubles.
+    """
+    # Get the expected function call string
+    call_str = str(sym)
+    if np.issubdtype(dtype, np.complexfloating):
+        # Complex functions have a 'c' prefix
+        call_str = 'c%s' % call_str
+    if dtype(0).real.itemsize <= 4:
+        # Single precision have an 'f' suffix (half is promoted to single)
+        call_str = '%sf' % call_str
+
+    # Operator setup
+    a = Symbol(name='a', dtype=dtype)
+    b = Scalar(name='b', dtype=dtype)
+
+    eq = Eq(a, sym(b))
+    op = Operator(eq)
+
+    # Ensure the generated function call has the correct form
+    assert call_str in str(op)
+
+
+@pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+def test_complex_override(dtype: np.dtype[np.complexfloating]) -> None:
+    """
+    Tests overriding complex values in op.apply().
+    """
+    grid = Grid(shape=(5, 5))
+    x, y = grid.dimensions
+
+    c = Constant(name='c', dtype=dtype, value=1.0 + 0.0j)
+    u = Function(name='u', grid=grid, dtype=dtype)
+    eq = Eq(u, x * x.spacing + c * y * y.spacing)
+    op = Operator(eq)
+    op.apply(c=2.0 + 1.0j)
+
+    # Check against numpy result
+    dx, dy = grid.spacing_map.values()
+    xx, yy = np.meshgrid(np.linspace(0, 4, 5, dtype=dtype),
+                         np.linspace(0, 4, 5, dtype=dtype))
+    expected = xx * dx + yy * dy * dtype(2.0 + 1.0j)
+    assert np.allclose(u.data.T, expected)
+
+
+@pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+def test_complex_time_deriv(dtype: np.dtype[np.complexfloating]) -> None:
+    """
+    Tests taking the time derivative of a complex-valued function.
+    """
+    grid = Grid(shape=(5, 5))
+    x, y = grid.dimensions
+    t = grid.time_dim
+
+    f = TimeFunction(name='f', grid=grid, space_order=2, dtype=dtype)
+    g = Function(name='g', grid=grid, dtype=dtype)
+    eqns = [Eq(f.forward, t * x * x.spacing * (1.0 + 0.0j) +
+               t * y * y.spacing * (0.0 + 1.0j)),
+            Eq(g, f.dt)]
+    op = Operator(eqns)
+    op.apply(time=10, dt=1.0)
+
+    # Check against expected result
+    dx, dy = grid.spacing_map.values()
+    xx, yy = np.meshgrid(np.linspace(0, 4, 5, dtype=dtype),
+                         np.linspace(0, 4, 5, dtype=dtype))
+    expected = xx * dx + yy * dy * dtype(0.0 + 1.0j)
+    assert np.allclose(g.data.T, expected)
+
+
+@pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+def test_complex_space_deriv(dtype: np.dtype[np.complexfloating]) -> None:
+    """
+    Tests taking the space derivative of a complex-valued function, with
+    respect to the real and imaginary axes.
+    """
+    grid = Grid(shape=(7, 7), dtype=dtype)
+    x, y = grid.dimensions
+
+    # Operator setup
+    f = Function(name='f', grid=grid, space_order=2)
+    g = Function(name='g', grid=grid)
+    h = Function(name='h', grid=grid)
+    eqns = [Eq(f, x * x.spacing + y * y.spacing),
+            Eq(g, f.dx, subdomain=grid.interior),
+            Eq(h, f.dy, subdomain=grid.interior)]
+    op = Operator(eqns)
+
+    dx = 1.0 + 0.0j
+    dy = 0.0 + 1.0j
+    op.apply(h_x=dx, h_y=dy)
+
+    # Check against expected result (1 within the interior)
+    dfdx = g.data.T[1:-1, 1:-1]
+    dfdy = h.data.T[1:-1, 1:-1]
+    assert np.allclose(dfdx, np.ones((5, 5), dtype=dtype))
+    assert np.allclose(dfdy, np.ones((5, 5), dtype=dtype))
diff --git a/tests/test_gpu_common.py b/tests/test_gpu_common.py
index a22ab93df4..13239687bc 100644
--- a/tests/test_gpu_common.py
+++ b/tests/test_gpu_common.py
@@ -2,12 +2,13 @@
 
 import pytest
 import numpy as np
+import sympy
 import scipy.sparse
 
 from conftest import assert_structure
 from devito import (Constant, Eq, Inc, Grid, Function, ConditionalDimension,
                     Dimension, MatrixSparseTimeFunction, SparseTimeFunction,
-                    SubDimension, SubDomain, SubDomainSet, TimeFunction,
+                    SubDimension, SubDomain, SubDomainSet, TimeFunction, exp,
                     Operator, configuration, switchconfig, TensorTimeFunction,
                     Buffer, assign)
 from devito.arch import get_gpu_info, get_cpu_info, Device, Cpu64
@@ -76,6 +77,25 @@ def test_maxpar_option(self):
         assert trees[0][0] is trees[1][0]
         assert trees[0][1] is not trees[1][1]
 
+    @pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+    def test_complex(self, dtype):
+        grid = Grid((5, 5))
+        x, y = grid.dimensions
+
+        c = Constant(name='c', dtype=dtype)
+        u = Function(name="u", grid=grid, dtype=dtype)
+
+        eq = Eq(u, x + sympy.I*y + exp(sympy.I + x.spacing) * c)
+        op = Operator(eq)
+        op(c=1.0 + 2.0j)
+
+        # Check against numpy
+        dx = grid.spacing_map[x.spacing]
+        xx, yy = np.meshgrid(np.linspace(0, 4, 5), np.linspace(0, 4, 5))
+        npres = xx + 1j*yy + np.exp(1j + dx) * (1.0 + 2.0j)
+
+        assert np.allclose(u.data, npres.T, rtol=5e-7, atol=0)
+
 
 class TestPassesOptional:
 
diff --git a/tests/test_gpu_openacc.py b/tests/test_gpu_openacc.py
index bdf732a12d..8c4813db0b 100644
--- a/tests/test_gpu_openacc.py
+++ b/tests/test_gpu_openacc.py
@@ -2,7 +2,7 @@
 import numpy as np
 
 from devito import (Grid, Function, TimeFunction, SparseTimeFunction, Eq, Operator,
-                    norm, solve)
+                    norm, solve, Max)
 from conftest import skipif, assert_blocking, opts_device_tiling
 from devito.data import LEFT
 from devito.exceptions import InvalidOperator
@@ -171,6 +171,17 @@ def test_multi_tile_blocking_structure(self):
             assert len(iters) == len(v)
             assert all(i.step == j for i, j in zip(iters, v))
 
+    def test_std_max(self):
+        grid = Grid(shape=(3, 3, 3))
+        x, y, z = grid.dimensions
+
+        u = Function(name='u', grid=grid)
+
+        op = Operator(Eq(u, Max(1.2 * x / y, 2.3 * y / x)),
+                      platform='nvidiaX', language='openacc')
+
+        assert '<algorithm>' in str(op)
+
 
 class TestOperator:
 
diff --git a/tests/test_grid.py b/tests/test_grid.py
new file mode 100644
index 0000000000..5753e17d7b
--- /dev/null
+++ b/tests/test_grid.py
@@ -0,0 +1,27 @@
+import numpy as np
+import pytest
+
+from devito import Grid
+
+
+# Unsigned ints are unreasonable but not necessarily invalid
+@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64, np.longdouble,
+                                   np.complex64, np.complex128, np.int8, np.int16,
+                                   np.int32, np.int64, np.uint8, np.uint16, np.uint32,
+                                   np.uint64])
+def test_extent_dtypes(dtype: np.dtype[np.number]) -> None:
+    """
+    Test that grid spacings are correctly computed for different dtypes.
+    """
+
+    # Construct a grid with the dtype and retrieve the spacing values
+    extent = (1, 1j) if np.issubdtype(dtype, np.complexfloating) else (2, 4)
+    grid = Grid(shape=(5, 5), extent=extent, dtype=dtype)
+    dx, dy = grid.spacing_map.values()
+
+    # Check that the spacings have the correct dtype
+    assert dx.dtype == dy.dtype == dtype
+
+    # Check that the spacings have the correct values
+    assert dx == dtype(extent[0] / 4)
+    assert dy == dtype(extent[1] / 4)
diff --git a/tests/test_ir.py b/tests/test_ir.py
index e6ea3f89e3..350de3ae20 100644
--- a/tests/test_ir.py
+++ b/tests/test_ir.py
@@ -5,6 +5,7 @@
 from conftest import EVAL, skipif  # noqa
 from devito import (Eq, Inc, Grid, Constant, Function, TimeFunction, # noqa
                     Operator, Dimension, SubDimension, switchconfig)
+from devito.ir.cgen import ccode
 from devito.ir.equations import LoweredEq
 from devito.ir.equations.algorithms import dimension_sort
 from devito.ir.iet import Iteration, FindNodes
@@ -14,7 +15,7 @@
 from devito.ir.support.space import (NullInterval, Interval, Forward, Backward,
                                      IterationSpace)
 from devito.ir.support.guards import GuardOverflow
-from devito.symbolics import DefFunction, FieldFromPointer, ccode
+from devito.symbolics import DefFunction, FieldFromPointer
 from devito.tools import prod
 from devito.types import Array, CriticalRegion, Jump, Scalar, Symbol
 
diff --git a/tests/test_linearize.py b/tests/test_linearize.py
index 1b531e6ab8..56f31ebe1f 100644
--- a/tests/test_linearize.py
+++ b/tests/test_linearize.py
@@ -190,7 +190,7 @@ def test_codegen_quality0(mode):
     # Only four access macros necessary, namely `uL0`, `bufL0`, `bufL1`
     # for the efunc args
     # (the other three obviously are _POSIX_C_SOURCE, START, STOP)
-    assert len(op._headers) == 6
+    assert len(op.headers) == 6
 
 
 def test_codegen_quality1():
@@ -212,7 +212,7 @@ def test_codegen_quality1():
 
     # Only two access macros necessary, namely `uL0` and `r1L0` (the other five
     # obviously are _POSIX_C_SOURCE, MIN, MAX, START, STOP)
-    assert len(op._headers) == 6
+    assert len(op.headers) == 6
 
 
 def test_pow():
diff --git a/tests/test_mpi.py b/tests/test_mpi.py
index fed3ae0905..8761069ff4 100644
--- a/tests/test_mpi.py
+++ b/tests/test_mpi.py
@@ -21,10 +21,7 @@
 from devito.tools import Bunch
 
 from examples.seismic.acoustic import acoustic_setup
-try:
-    from tests.test_dse import TestTTI
-except ImportError:
-    TestTTI = None
+from test_dse import TestTTI
 
 
 class TestDistributor:
diff --git a/tests/test_operator.py b/tests/test_operator.py
index d5759c1c92..edd5e9ec9c 100644
--- a/tests/test_operator.py
+++ b/tests/test_operator.py
@@ -9,14 +9,15 @@
                     SparseFunction, SparseTimeFunction, Dimension, error, SpaceDimension,
                     NODE, CELL, dimensions, configuration, TensorFunction,
                     TensorTimeFunction, VectorFunction, VectorTimeFunction,
-                    div, grad, switchconfig)
+                    div, grad, switchconfig, exp)
 from devito import  Inc, Le, Lt, Ge, Gt  # noqa
 from devito.exceptions import InvalidOperator
 from devito.finite_differences.differentiable import diff2sympy
 from devito.ir.equations import ClusterizedEq
 from devito.ir.equations.algorithms import lower_exprs
 from devito.ir.iet import (Callable, Conditional, Expression, Iteration, TimedList,
-                           FindNodes, IsPerfectIteration, retrieve_iteration_tree)
+                           FindNodes, IsPerfectIteration, retrieve_iteration_tree,
+                           FindSymbols)
 from devito.ir.support import Any, Backward, Forward
 from devito.passes.iet.languages.C import CDataManager
 from devito.symbolics import ListInitializer, indexify, retrieve_indexed
@@ -348,6 +349,19 @@ def test_nested_lowering_indexify(self):
         assert np.all(u0.data[1, :] == 4)
         assert np.all(u0.data[2, :] == 8)
 
+    def test_scalar_type(self):
+        grid = Grid(shape=(4, 4), dtype=np.float32)
+        u = Function(name='u', grid=grid, space_order=4)
+
+        eq = Eq(u, u.laplace)
+        op0 = Operator(eq)
+        scalars = [s for s in FindSymbols().visit(op0) if s.name.startswith('r')]
+        assert all(s.dtype == np.float32 for s in scalars)
+
+        op1 = Operator(eq, opt=('advanced', {'scalar-min-type': np.float64}))
+        scalars = [s for s in FindSymbols().visit(op1) if s.name.startswith('r')]
+        assert all(s.dtype == np.float64 for s in scalars)
+
 
 class TestArithmetic:
 
@@ -640,6 +654,25 @@ def test_tensor(self, func1):
         op2 = Operator([Eq(f, f.dx) for f in f1.values()])
         assert str(op1.ccode) == str(op2.ccode)
 
+    @pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+    def test_complex(self, dtype):
+        grid = Grid((5, 5))
+        x, y = grid.dimensions
+
+        c = Constant(name='c', dtype=dtype)
+        u = Function(name="u", grid=grid, dtype=dtype)
+
+        eq = Eq(u, x + sympy.I*y + exp(sympy.I + x.spacing) * c)
+        op = Operator(eq)
+        op(c=1.0 + 2.0j)
+
+        # Check against numpy
+        dx = grid.spacing_map[x.spacing]
+        xx, yy = np.meshgrid(np.linspace(0, 4, 5), np.linspace(0, 4, 5))
+        npres = xx + 1j*yy + np.exp(1j + dx) * (1.0 + 2.0j)
+
+        assert np.allclose(u.data, npres.T, rtol=1e-7, atol=0)
+
 
 class TestAllocation:
 
@@ -724,10 +757,10 @@ def verify_parameters(self, parameters, expected):
         """
         boilerplate = ['timers']
         parameters = [p.name for p in parameters]
-        for exp in expected:
-            if exp not in parameters + boilerplate:
-                error("Missing parameter: %s" % exp)
-            assert exp in parameters + boilerplate
+        for expi in expected:
+            if expi not in parameters + boilerplate:
+                error("Missing parameter: %s" % expi)
+            assert expi in parameters + boilerplate
         extra = [p for p in parameters if p not in expected and p not in boilerplate]
         if len(extra) > 0:
             error("Redundant parameters: %s" % str(extra))
diff --git a/tests/test_pickle.py b/tests/test_pickle.py
index 373b4dca29..df8c3a79b2 100644
--- a/tests/test_pickle.py
+++ b/tests/test_pickle.py
@@ -1,3 +1,4 @@
+import ctypes
 import pickle as pickle0
 import cloudpickle as pickle1
 
@@ -22,7 +23,8 @@
 from devito.types.basic import BoundSymbol, AbstractSymbol
 from devito.tools import EnrichedTuple
 from devito.symbolics import (IntDiv, ListInitializer, FieldFromPointer,
-                              CallFromPointer, DefFunction)
+                              CallFromPointer, DefFunction, Cast, SizeOf,
+                              pow_to_mul)
 from examples.seismic import (demo_model, AcquisitionGeometry,
                               TimeAxis, RickerSource, Receiver)
 
@@ -581,14 +583,45 @@ def test_equation(self, pickle):
 
         eq = Eq(f, f+1, implicit_dims=xs)
 
-        pkl_eq = pickle0.dumps(eq)
-        new_eq = pickle0.loads(pkl_eq)
+        pkl_eq = pickle.dumps(eq)
+        new_eq = pickle.loads(pkl_eq)
 
         assert new_eq.lhs.name == f.name
         assert str(new_eq.rhs) == 'f(x) + 1'
         assert new_eq.implicit_dims[0].name == 'xs'
         assert new_eq.implicit_dims[0].factor.data == 4
 
+    @pytest.mark.parametrize('typ', [ctypes.c_float, 'struct truct'])
+    def test_Cast(self, pickle, typ):
+        a = Symbol('a')
+        un = Cast(a, dtype=typ)
+
+        pkl_un = pickle.dumps(un)
+        new_un = pickle.loads(pkl_un)
+
+        assert un == new_un
+
+    @pytest.mark.parametrize('typ', [ctypes.c_float, 'struct truct'])
+    def test_SizeOf(self, pickle, typ):
+        un = SizeOf(typ)
+
+        pkl_un = pickle.dumps(un)
+        new_un = pickle.loads(pkl_un)
+
+        assert un == new_un
+
+    def test_pow_to_mul(self, pickle):
+        grid = Grid(shape=(3,))
+        f = Function(name='f', grid=grid)
+        expr = pow_to_mul(f ** 2)
+
+        assert expr.is_Mul
+
+        pkl_expr = pickle.dumps(expr)
+        new_expr = pickle.loads(pkl_expr)
+
+        assert new_expr.is_Mul
+
 
 class TestAdvanced:
 
diff --git a/tests/test_symbolics.py b/tests/test_symbolics.py
index 2bae5679c8..9644270a6f 100644
--- a/tests/test_symbolics.py
+++ b/tests/test_symbolics.py
@@ -9,12 +9,12 @@
                     Operator, SubDimension, norm, Le, Ge, Gt, Lt, Abs, sin, cos,
                     Min, Max)
 from devito.finite_differences.differentiable import SafeInv, Weights
-from devito.ir import Expression, FindNodes
+from devito.ir import Expression, FindNodes, ccode
 from devito.symbolics import (retrieve_functions, retrieve_indexed, evalrel,  # noqa
                               CallFromPointer, Cast, DefFunction, FieldFromPointer,
                               INT, FieldFromComposite, IntDiv, Namespace, Rvalue,
-                              ReservedWord, ListInitializer, ccode, uxreplace,
-                              retrieve_derivatives)
+                              ReservedWord, ListInitializer, uxreplace,
+                              retrieve_derivatives, BaseCast)
 from devito.tools import as_tuple
 from devito.types import (Array, Bundle, FIndexed, LocalObject, Object,
                           ComponentAccess, StencilDimension, Symbol as dSymbol)
@@ -112,6 +112,19 @@ def test_modified_sympy_assumptions():
     assert s2 == s1
 
 
+def test_real():
+    for dtype in [np.float32, np.complex64]:
+        c = Constant(name='c', dtype=dtype)
+        assert c.is_real is not np.iscomplexobj(dtype(0))
+        assert c.is_imaginary is np.iscomplexobj(dtype(0))
+        f = Function(name='f', dtype=dtype, grid=Grid((11,)))
+        assert f.is_real is not np.iscomplexobj(dtype(0))
+        assert f.is_imaginary is np.iscomplexobj(dtype(0))
+        s = dSymbol(name='s', dtype=dtype)
+        assert s.is_real is not np.iscomplexobj(dtype(0))
+        assert s.is_imaginary is np.iscomplexobj(dtype(0))
+
+
 def test_constant():
     c = Constant(name='c')
 
@@ -409,8 +422,8 @@ def test_rvalue():
 def test_cast():
     s = Symbol(name='s', dtype=np.float32)
 
-    class BarCast(Cast):
-        _base_typ = 'bar'
+    class BarCast(BaseCast):
+        _dtype = 'bar'
 
     v = BarCast(s, '**')
     assert ccode(v) == '(bar**)s'