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

GPU Coder	Generate GPU code from MATLAB code
Check GPU Install	Verify and set up the GPU code generation environment

Functions

`codegen`	Generate C/C++ code from MATLAB code
`gpucoder`	Open GPU Coder app
`coder.gpu.kernel`	Pragma that maps `for`-loops to GPU kernels
`coder.gpu.kernelfun`	Pragma that maps function to GPU kernels
`coder.gpu.nokernel`	Pragma to disable kernel creation for loops
`gpucoder.profile`	Create an execution profile report for the generated CUDA code

Objects

`coder.gpuConfig`	Configuration parameters for CUDA code generation from MATLAB code by using GPU Coder
`coder.CodeConfig`	Configuration parameters for C/C++ code generation from MATLAB code
`coder.EmbeddedCodeConfig`	Configuration parameters for C/C++ code generation from MATLAB code with Embedded Coder
`coder.gpuEnvConfig`	Create configuration object containing the parameters passed to `coder.checkGpuInstall` for performing GPU code generation environment checks

Topics

Workflow

GPU Coder troubleshooting workflow.

Code Generation Reports

Create and view reports generated during code generation.

Trace Between Generated CUDA Code and MATLAB Source Code

Highlight sections of MATLAB code that runs on the GPU.

Generating a GPU Code Metrics Report for Code Generated from MATLAB Code

Create and explore GPU static code metrics report.

Kernel Analysis

Recommendations for generating efficient CUDA kernels.

Memory Bottleneck Analysis

Reduce memory bottleneck issues when using GPU Coder.

Analyze Execution Profiles of the Generated Code

Fine-grain profiling for the MATLAB algorithm and its generated CUDA code through SIL.

Analysis with NVIDIA Profiler

Improve performance by using the information obtained from NVIDIA Profiler (nvvp).

GPU Coder Limitations

See current limitations of GPU Coder.

Featured Examples

GPU Execution Profiling of the Generated Code

Generate an execution profiling report for the generated CUDA® code by using the gpucoder.profile function. Fog rectification is used as an example to demonstrate this concept.

Open Script

Documentation