When you convert MATLAB code to C/C++ using MATLAB Coder, the parallelism from high-level constructs like parpool or gpuArray isn't automatically translated to parallel C/C++ code. To run code in parallel in C/C++, you need to use libraries or APIs designed for this purpose.
Here are steps to parallelize the generated C/C++ code in Visual Studio:
- Review the Generated Code: Check the generated code to understand its structure and where parallelism might be beneficial.
- Parallel Libraries in C/C++:
- Use OpenMP for CPU multi-threading. It's supported by Visual Studio and allows you to add parallelism with simple #pragma directives.
- For GPU acceleration, you can use CUDA (if the GPU is from NVIDIA) or OpenCL. These are more complex and require a good understanding of GPU programming.
- OpenMP Example:
- Enable OpenMP in Visual Studio by going to Project Properties -> C/C++ -> Language -> OpenMP Support.
- Add OpenMP directives to your C/C++ code:
#pragma omp parallel for
for (int i = 0; i < N; ++i) {
// Your parallel code here
}
- CUDA or OpenCL:
- If you used gpuArray in MATLAB, this indicates that the code may benefit from GPU acceleration.
- Learn the basics of CUDA or OpenCL to understand how to offload computations to the GPU.
- Write CUDA kernels or OpenCL programs that reflect the operations you performed with gpuArray.
- Manual Conversion:
- Identify the parts of the MATLAB code that were running under parpool or using gpuArray.
- Manually convert these parts to use OpenMP, CUDA, or OpenCL.
- Testing and Profiling:
- After adding parallelism, test the code thoroughly to ensure it behaves correctly.
- Use profiling tools to understand performance bottlenecks and optimize the parallel sections.
- Integration:
- Make sure that your parallel code is well-integrated with the rest of the C/C++ code that was generated by MATLAB Coder.
- Ensure proper data transfer between CPU and GPU if using CUDA or OpenCL.
Keep in mind that MATLAB's parallel computing toolbox and GPU support handle a lot of complexity behind the scenes. When converting to C/C++, you'll need to handle this complexity manually. It's a non-trivial task that requires good knowledge of parallel programming paradigms and the associated C/C++ libraries or extensions.
Also, the C code generated by MATLAB Coder is not meant to be human-readable or human-editable, it is meant to be functionally equivalent to the original MATLAB code. If you need to add parallelism to the generated code, it might be better to write new code that calls the generated code where necessary but manages parallelism separately in a way that you can control and understand.
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