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Performance

Troubleshoot code generation issues, improve code execution time, and reduce memory usage of generated code

Some of the most common reasons why GPU Coder™ generated code is not performing as expected are:

  • CUDA® kernels are not created.

  • Host to device and device to host memory transfers (cudaMemcpy) are throttling performance.

  • Not enough parallelism or device issues.

These topics elaborate on the common causes for these symptoms and describe how to utilize the built-in screener to detect these issues. You can find information on how to work around for these issues and generate more efficient CUDA code.

Apps

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GPU CoderGenerate GPU code from MATLAB code
GPU Environment CheckVerify and set up GPU code generation environment

Functions

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codegenGenerate C/C++ code from MATLAB code
gpucoderOpen GPU Coder app
gpucoder.profileCreate an execution profile report for generated CUDA code
coder.gpu.kernelPragma that maps for-loops to GPU kernels
coder.gpu.kernelfunPragma that maps function to GPU kernels
coder.gpu.nokernelPragma to disable kernel creation for loops

Objects

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coder.gpuConfigConfiguration parameters for CUDA code generation from MATLAB code by using GPU Coder
coder.CodeConfigConfiguration parameters for C/C++ code generation from MATLAB code
coder.EmbeddedCodeConfigConfiguration parameters for C/C++ code generation from MATLAB code with Embedded Coder
coder.gpuEnvConfigCreate configuration object containing the parameters passed to coder.checkGpuInstall for performing GPU code generation environment checks

Topics