-
Notifications
You must be signed in to change notification settings - Fork 1
/
drgpu_entry.py
executable file
·161 lines (142 loc) · 6.9 KB
/
drgpu_entry.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
#!/bin/python3
from gather import *
import argparse
import os
from unit_hunt import *
from dot_graph import *
from suggestions import *
from read_reports import *
from source_code_analysis import add_source_code_nodes
def work(report, dot_graph_name, memoryconfig):
global memory_metrics
read_config(memoryconfig, config)
analysis = Analysis()
# {stat_name: stat, } type:{str: Stat}
all_stats = analysis.all_stats
if dot_graph_name is None:
(_, dot_graph_name) = os.path.split(report.path)
(dot_graph_name, _) = os.path.splitext(dot_graph_name)
# read reports and filter all useful stats
fill_stats(all_stats, report)
if report.source_report_path:
fill_source_report(report, analysis)
hw_tree = Node('Idle')
hw_tree.suffix_label = ' of total cycles'
retireIPC = all_stats.get('retireIPC', None)
if retireIPC:
root_percentage = retireIPC.value / config.quadrants_per_SM
else:
print("Could not get stat retireIPC")
root_percentage = 0
hw_tree.percentage = 1 - root_percentage
hw_tree.prefix_label = get_kernel_name(all_stats['kernel_name'].value) + "\n"
hw_tree.suffix_label = ''
best_possible = 100 * (
1.0 - 1.0 / (np.ceil(all_stats['activewarps_per_activecycle'].value / config.quadrants_per_SM)))
hw_tree.suffix_label += r" (lowest possible: %i%% for %i active warps)" % (
best_possible, all_stats['activewarps_per_activecycle'].value)
max_val = 0
sol_unit = ""
for unit in ['SM', 'L1', 'L2', 'Dram', 'Compute_Memory']:
next_val = all_stats['sol_' + unit.lower()].value
if next_val > max_val:
sol_unit = unit
max_val = next_val
hw_tree.suffix_label += r"\nUtil/SOL: %.2f%% (%s)" % (max_val, sol_unit)
hw_tree.suffix_label += r"\nIssue IPC: %.2f" % (all_stats["issueIPC"].value)
# first level
tmpstats = warp_cant_issue(all_stats)
add_sub_branch(tmpstats, hw_tree, 1)
if report.source_report_path is not None:
add_source_code_nodes(tmpstats, hw_tree, analysis)
# pipe utilization is the subbranch of shadow_pipe_throttle
tmpstats = pipe_utilization(all_stats)
target_node = find_node(hw_tree, "warp_cant_issue_pipe_throttle")
if not target_node:
print("Could not find the target node: warp_cant_issue_pipe_throttle")
else:
add_pipe_throttle_branch(tmpstats, target_node)
# instruction distribution is the subbranch of wait
tmpstats = instruction_distribution(all_stats)
target_node = find_node(hw_tree, "warp_cant_issue_wait")
if not target_node:
print("Could not find the target node: warp_cant_issue_wait")
else:
add_sub_branch(tmpstats, target_node, 1)
# warp_cant_issue_dispatch_stall
tmpstats = cant_dispatch(all_stats)
target_node = find_node(hw_tree, "warp_cant_issue_dispatch")
if not target_node:
print("Could not find the target node: warp_cant_issue_dispatch")
else:
add_sub_branch(tmpstats, target_node, 1)
target_node = find_node(hw_tree, "warp_cant_issue_lg_throttle")
if not target_node:
print("Could not find the target node: warp_cant_issue_lg_throttle")
else:
add_lg_throttle_branch(all_stats, target_node)
# target_node = find_node(hw_tree, "warp_cant_issue_barrier")
# if not target_node:
# print("Could not find the target node: warp_cant_issue_barrier")
# else:
# add_sub_branch(tmpstats, target_node, 1)
# warp_cant_issue_long_scoreboard memory
bottleneck_unit, bottleneck_stats, memory_metrics = long_scoreboard_throughput(all_stats, memory_metrics)
long_scoreboard_node = find_node(hw_tree, "warp_cant_issue_long_scoreboard")
latency_stats = long_scoreboard_latency(all_stats, memory_metrics)
add_sub_branch_for_longscoreboard_latency(latency_stats, long_scoreboard_node, all_stats, memory_metrics)
add_sub_branch_for_longscoreboard_throughput(all_stats, bottleneck_unit, bottleneck_stats, long_scoreboard_node, 1)
shared_mem_stats = common_function_pattern(all_stats, 'shared_ld_(\d+)b_executed')
add_shared_memory_info(all_stats, shared_mem_stats, memory_metrics)
target_node = find_node(hw_tree, "warp_cant_issue_mio_throttle")
add_branch_for_mio_throttle(all_stats, shared_mem_stats, memory_metrics, target_node)
target_node = find_node(hw_tree, "warp_cant_issue_short_scoreboard")
add_branch_for_short_scoreboard(all_stats, shared_mem_stats, memory_metrics, target_node)
# suggestions part
pipe_suggest(hw_tree, all_stats)
barrier_suggest(hw_tree, all_stats)
branch_solving_suggest(hw_tree, all_stats)
dispatch_stall_suggest(hw_tree, all_stats)
drain_suggest(hw_tree, all_stats)
# imc_miss_suggest(hw_tree, all_stats)
lg_credit_throttle_suggest(hw_tree, all_stats)
memory_suggest(hw_tree, all_stats, bottleneck_unit, memory_metrics)
membar_suggest(hw_tree, all_stats)
mio_throttle_suggest(hw_tree, all_stats, shared_mem_stats)
short_scoreboard_suggest(hw_tree, all_stats, shared_mem_stats)
wait_suggestion(hw_tree, all_stats)
build_dot_graph(hw_tree, "dots/" + dot_graph_name)
print("save to dots/" + dot_graph_name + ".svg")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--report-path', metavar='The path of main report.',
required=True, dest='report_path', action='store')
parser.add_argument('-o', '--output', metavar='Set the output file to save decision tree.',
required=False, dest='output', action='store')
parser.add_argument('-s', '--source', metavar='The path of source mapping report from NCU. NCU model only.',
required=False, dest='source', action='store')
parser.add_argument('-c', '--memoryconfig',
metavar='The path of memory config file or only file name in mem_config folder',
required=False, dest='memoryconfig', action='store')
parser.add_argument('-id', '--id',
metavar='The id of the kernel you want to analyze in exported csv files.',
required=False, dest='kernel_id', action='store')
args = parser.parse_args()
report_path = args.report_path
if not args.memoryconfig:
memoryconfig = sys.path[0] + '/mem_config/gtx1650.ini'
print(
"You didn't specify running platform for this report. DrGPU will use gtx1650.ini as default GPU configuration.")
else:
memoryconfig = args.memoryconfig
if not memoryconfig.endswith('.ini'):
memoryconfig += '.ini'
if not memoryconfig.startswith('/'):
memoryconfig = sys.path[0] + "/mem_config/" + memoryconfig
kernel_id = 0
if args.kernel_id:
kernel_id = int(args.kernel_id)
print(report_path)
print(args.source)
report = Report(report_path, args.source, kernel_id)
work(report, args.output, memoryconfig)