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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
	verify and set up gpu code generation environment

functions

code generation

`codegen`	generate c/c code from matlab code
	open gpu coder app
	analyze and optimize performance of the generated code

programming for code generation

	pragma that maps `for`-loops to gpu kernels
	pragma that maps function to gpu kernels
	pragma to disable kernel creation for loops

objects

code configuration

	configuration parameters for cuda code generation from matlab code by using gpu coder
	configuration parameters for c/c code generation from matlab code
	configuration parameters for c/c code generation from matlab code with embedded coder
	create configuration object containing the parameters passed to `coder.checkgpuinstall` for performing gpu code generation environment checks

topics

gpu coder troubleshooting workflow.
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.
create and explore gpu static code metrics report.
visualize code metrics and identify optimization and tuning opportunities in your code.
suggestions for debugging cuda mex function.
recommendations for generating efficient cuda kernels.
reduce memory bottleneck issues when using gpu coder.
improve performance by using the information obtained from nvidia profiler (nvvp).
troubleshoot compilation failures due to a register count nvlink error.

featured examples

analyze performance of the generated cuda code

analyze and optimize the performance of the generated cuda® code by using the gpuperformanceanalyzer function.