BLAS and LAPACK External Library Callbacks for Code Generation

The MATLAB-Coder-integration-for-BLAS-LAPACK repository provides a collection of MATLAB® Coder™ callback classes designed to interface with popular BLAS and LAPACK libraries, such as OpenBLAS and Intel® oneMKL, to improve performance in standalone code generation.

Highlights

• Ease of Use — Integrate external libraries with preconfigured callbacks.
• Performance Boost — Increase the speed of generated code for matrix operations by using optimized external libraries.
• Flexibility — Switch between different BLAS and LAPACK implementations as required.
• Compatibility — Use with both MATLAB Coder and Simulink® Coder™ for code generation.

Table of Contents

• Introduction
• Example
• Installation
• Usage Instructions
• Setting the BLAS/LAPACK Installation Path
• Supported Libraries and Platforms
• Versions Used for Testing
• Further Reading and Documentation
• Contact Us

Introduction

The MATLAB-Coder-integration-for-BLAS-LAPACK repository offers a collection of callback classes for MATLAB Coder designed to interface with high-performance external libraries that implement BLAS and LAPACK. By leveraging these libraries, the project aims to enhance the performance of generated code that involves matrix and vector operations. You can use the callback classes for code generation with MATLAB Coder and Simulink Coder. Additionally, these callback classes can serve as examples for creating new callbacks not currently included in the repository. The external libraries are linked statically when the 'lib' code generation configuration (coder.config('lib')) and dynamically when you use the 'dll' or 'exe' code generation configuration (coder.config('dll') or coder.config('exe')), wherever possible.

This table summarizes the supported libraries and platforms:

Library	Windows®	Mac	Linux®	Raspberry Pi®
oneMKL	✅		✅
OpenBLAS	✅	✅	✅	✅

This repository provides only the callback classes; you need to install the third-party libraries yourself. See Supported Libraries and Platforms, for more information on using these libraries and choosing the most suitable library for your needs.

Example

This example shows how to use the callback classes provided in this repository for code generation using the MATLAB function invWrapper.m:

function out = invWrapper(in)
    out = inv(in);
end

Generate code using these commands:

% Create a configuration that does not use BLAS or LAPACK callbacks
cfg1 = coder.config("lib");

% Create a configuration with oneMKL BLAS and LAPACK callback classes on linux
cfg2 = coder.config("lib");
cfg2.CustomBLASCallback = "oneMKL.linux.OpenMPBLASCallback";
cfg2.CustomLAPACKCallback = "oneMKL.linux.OpenMPLAPACKCallback";

% Generate the input
rng(11, "twister");
A = rand(4000, 4000);

% Code generation example
codegen invWrapper -args {A} -config cfg2

% Performance comparison
tWithoutCallback = coder.timeit("invWrapper", 1, {A}, 'CoderConfig', cfg1, 'CompileArgs', {coder.typeof(1, [Inf, Inf])});
tWithCallback = coder.timeit("invWrapper", 1, {A}, 'CoderConfig', cfg2, 'CompileArgs', {coder.typeof(1, [Inf, Inf])});

fprintf("Time taken in seconds by codegen without callback: %f\n", tWithoutCallback);
fprintf("Time taken in secondd by codegen with oneMKL callback: %f\n", tWithCallback);

Output on a 6-core, 12-thread Intel Xeon® W-2133 CPU running Debian® 12:

Time taken in seconds by codegen without callback: 63.879588
Time taken in seconds by codegen with oneMKL callback: 11.165440

Installation

To get started with the MATLAB-Coder-integration-for-BLAS-LAPACK repository, you can either clone the repository using Git or download it as a ZIP file.

Clone the Repository

Clone the repository by using this command at the command line (this requires Git to be installed):

git clone https://github.com/mathworks/MATLAB-Coder-integration-for-BLAS-LAPACK.git

Download as ZIP

Download the code as a ZIP file (main.zip) from this repository and unzip it to your desired location.

Usage Instructions

To use the callback classes with MATLAB Coder, add the folder containing the callback classes to the MATLAB path. Use this command, modifying the path as needed:

addpath("C:\Users\<USER>\Downloads\MATLAB-Coder-integration-for-BLAS-LAPACK-main");

After adding the repository to the path, you can use the required callback class depending on the BLAS and LAPACK library installed locally. The callback classes work without any additional changes if the installation paths of the libraries match the paths used in the callback classes. To update the installation paths, see Setting the BLAS/LAPACK Installation Path.

Using the Callback Classes

MATLAB Command Line or Script

Use the callback class directly from the command line or in a script by modifying the CustomBLASCallback and CustomLAPACKCallback properties of the coder configuration, as shown:

cfg = coder.config('dll');
cfg.CustomBLASCallback = "oneMKL.windows.OpenMPBLASCallback";
cfg.CustomLAPACKCallback = "oneMKL.windows.OpenMPLAPACKCallback";

codegen invWrapper -args {coder.typeof(1, [inf,inf])} -config cfg

Coder App

R2024b or earlier

To use the provided callbacks in the Coder app, select More Settings in the Generate Code. In the settings window, select All Settings in the left pane expand the Advanced section in the right pane and look for Custom BLAS library callback and Custom LAPACK library callback settings. Modify these settings to the required callback class.

R2025a or later

To use the provided callbacks with the Coder app, select Settings in the tool strip. In the settings window, select Custom Code in the left panel. Provide the callback classes in the Custom BLAS library callback and Custom LAPACK library callback text boxes.

Simulink

In Simulink, to use the callback classes with function blocks, in the Configuration Parameters dialog box, in the Code Generation pane, expand the Advanced Parameters section in the right pane. Update the Custom BLAS library callback and Custom LAPACK library callback settings to the required callback class.

Setting the BLAS/LAPACK Installation Path

The code generation command using the callback classes can fail if the installed path of BLAS and LAPACK differs from the path set in the callback classes. Follow the instructions below to set the installation path correctly for the callback classes. When the path is updated, it affects both BLAS and LAPACK callbacks for that library. Also, run clear all in the MATLAB command line to clear any cached path artifacts.

Use the setLibraryPath function, which allows you to specify the paths. The specified paths are stored in the paths.json file at the root of the repository. Storing the paths in a JSON file allows you to manage and update paths without altering code files and preserves the paths across multiple invocations of MATLAB.

Additionally, the callback classes check for the installation of necessary software by using environment variables. If relevant environment variables are set, the path is inferred from them and used automatically. The default priority for setting paths is:

1. File paths in the JSON file.
2. Paths constructed from environment variables.
3. Paths embedded in the repository are the last resort.

You can further use the setLibraryPath function to override the environment variables in cases where they fail to work.

Example Usage of setLibraryPath Function

setLibraryPath('OneAPIPath'='C:\Program Files (x86)\Intel\oneAPI');
% To remove a set variable
setLibraryPath('OneAPIPath'='');

On Windows

For OpenBLAS, the callback class checks for the OpenBLAS_HOME environment variable. If not found, it assumes the default installation path for OpenBLAS is C:\bin\OpenBLAS-0.3.28-x64. This can be overridden by updating the paths.json file using setLibraryPath('OpenBLASPath', 'C:\My\Custom\Path').

Similarly, for oneMKL, the callback class checks for environment variables ONEAPI_ROOT followed by MKLROOT (if ONEAPI_ROOT is not found). If the environment variables are not set the default path is assumed to be C:\Program Files (x86)\Intel\oneAPI. The environment variable and default path can be overridden by using the setLibraryPath function as follows, setLibraryPath('OneAPIPath', 'C:\My\Custom\oneAPI\Path').

On Linux, Mac, and Raspberry Pi

For OpenBLAS, the callback class checks for the OpenBLAS_HOME environment variable. If the variable is not found, it uses the default location /opt/OpenBLAS for Linux® and Raspberry Pi®. The default is set to /opt/homebrew/opt/openblas for Mac. To override the location, you can use the setLibraryPath function to set the OpenBLASPath value. For cases where the lib and include paths are not set to $OpenBLASPath/lib and $OpenBLASPath/include, respectively, the setLibraryPath function allows you to set OpenBLASLibPath and OpenBLASIncludePath separately. When the OpenBLASLibPath and OpenBLASIncludePath are set, the OpenBLASPath variable is ignored. In the case of Raspberry Pi, the paths correspond to the location of library and header files on the Raspberry Pi rather than the path on the host machine. When generating code for both the host machine and Raspberry Pi and using OpenBLAS, be careful to update the path values in the JSON file paths.json using the setLibraryPath function.

For oneMKL, the callback class checks for environment variables ONEAPI_ROOT followed by MKLROOT (if ONEAPI_ROOT is not found). If they are not set, it uses the default path /opt/intel/oneapi. If you have installed oneAPI at a different path and have not set the environment variables, you can use the setLibraryPath function to create or update a JSON file containing the path. For example, to set the path to /my/custom/path, use setLibraryPath('OneAPIPath', '/my/custom/path'). You can also use the setLibraryPath function to override the environment variables in cases where they do not work.

Supported Libraries and Platforms

Intel oneMKL

Follow the instructions on the Intel website to download and install Intel oneMKL. You can install oneMKL as part of the oneAPI toolkits or as a standalone version. The callback classes use the default path where oneMKL is typically installed. This repository provides callback classes that allow you to link to both the serial and parallel (OpenMP) versions of oneMKL's BLAS and LAPACK libraries. This functionality is available on Linux systems and on Windows systems when using the MSVC compiler. When performing matrix operations inside parallel loops, it is recommended to use the serial versions for best performance and the OpenMP parallel version otherwise. If you encounter header file or linker errors because the library was not found, see Setting the BLAS/LAPACK Installation Path. Note that after you encounter an error, you might have to run clear all before codegen to clear all cached paths.

Windows

• MSVC:
  • Sequential version — Use oneMKL.windows.SequentialBLASCallback and oneMKL.windows.SequentialLAPACKCallback for BLAS and LAPACK callbacks, respectively.
  • OpenMP version — Use oneMKL.windows.OpenMPBLASCallback and oneMKL.windows.OpenMPLAPACKCallback for BLAS and LAPACK callbacks, respectively.
• MinGW — For MinGW, the callback class is restricted to using only the sequential version due to ABI differences between the generated code and oneMKL libraries, which affect the OpenMP® libraries. The library is always dynamically linked.
  • Sequential version — Use oneMKL.windows.MinGWBLASCallback and oneMKL.windows.MinGWLAPACKCallback for BLAS and LAPACK callbacks, respectively.

Linux

• Sequential version — Use oneMKL.linux.SequentialBLASCallback and oneMKL.linux.SequentialLAPACKCallback for BLAS and LAPACK, respectively.
• OpenMP version — Use oneMKL.linux.OpenMPBLASCallback and oneMKL.linux.OpenMPLAPACKCallback for BLAS and LAPACK, respectively.

OpenBLAS

OpenBLAS is a popular open-source implementation of the BLAS and LAPACK libraries, designed for high performance. This section provides instructions for using OpenBLAS across platforms.

Windows

Binaries for OpenBLAS are available on SourceForge. Download the appropriate binary (for example, OpenBLAS-0.3.28-x64.zip) and then unzip or install it. Callback classes assume installation path is C:\bin\OpenBLAS-0.3.28-x64. If installed at a different path see Setting the BLAS/LAPACK Installation Path.

• MSVC — Use openBLAS.windows.MSVCBLASCallback and openBLAS.windows.MSVCLAPACKCallback. These callbacks dynamically link to OpenBLAS libraries due to ABI differences.
• MinGW — Use openBLAS.windows.MinGWBLASCallback and openBLAS.windows.MinGWLAPACKCallback.

Linux and Raspberry Pi

OpenBLAS can be installed through your package manager or from source. If you are using Debian® or Ubuntu®, the version installed by apt does not include the LAPACKE interface, which is necessary for MATLAB Coder. Only the BLAS callback class will work with this installation. To use the BLAS callback when OpenBLAS is installed from apt, use this command:

setLibraryPath('OpenBLASLibPath', '/usr/lib/x86_64-linux-gnu/openblas-openmp', 'OpenBLASIncludePath', '/usr/include/x86_64-linux-gnu/openblas-openmp')

For Raspberry Pi, use:

setLibraryPath('OpenBLASLibPath', '/usr/lib/aarch64-linux-gnu/openblas-openmp', 'OpenBLASIncludePath', '/usr/include/aarch64-linux-gnu/openblas-openmp')

When installed from source, by default, it installs to /opt/OpenBLAS. If the installation is at a different location, refer to the Setting the BLAS/LAPACK Installation Path section.

• Linux OpenMP version — Use openBLAS.linux.BLASCallback and openBLAS.linux.LAPACKCallback for BLAS and LAPACK calls, respectively.
• Raspberry Pi OpenMP version — Use openBLAS.raspberryPi.BLASCallback and openBLAS.raspberryPi.LAPACKCallback for BLAS and LAPACK calls, respectively.

macOS

Callback classes assume OpenBLAS is installed in /opt/homebrew/opt/openblas. If OpenBLAS is installed at a different location, set the environment variable or path accordingly, as described in Setting the BLAS/LAPACK Installation Path. On macOS, OpenMP installation is required. The callback class uses the OpenMP version that is included with MATLAB. If a custom OpenMP installation is present, modify the OmpPath property in +openBLAS/+mac/CallbackBase.m to point to the custom OpenMP folder.

• OpenMP version — Use openBLAS.mac.BLASCallback and openBLAS.mac.LAPACKCallback for BLAS and LAPACK calls, respectively.

Versions Used for Testing

The following software versions were used for testing:

• Intel oneMKL: Version 2024.2
• OpenBLAS: Version 0.3.28
• MATLAB: R2025a

The callback classes are expected to work with other versions as well.

Further Reading and Documentation

• Speed Up Matrix Operations in Generated Standalone Code by Using BLAS Calls

• Speed Up Linear Algebra in Generated Standalone Code by Using LAPACK Calls

• Speed Up Matrix Operations in Code Generated from a MATLAB Function Block

• coder.BLASCallback Class

• coder.LAPACKCallback Class

Contact Us

We encourage all feedback. If you encounter a technical issue or have an enhancement request, please create an issue here or contact MathWorks at [email protected].