Optimize MATLAB for GPU Code Generation

Iteratively optimize a MATLAB design file for GPU Coder: compile, benchmark, apply structural optimizations, profile with gpuPerformanceAnalyzer, fix diagnostics, and verify numerical equivalence at every step.

When to Use

• User has a MATLAB function and wants faster GPU MEX or CUDA code
• User mentions GPU Coder, codegen, CUDA, gpuPerformanceAnalyzer
• User asks to profile generated GPU/CUDA code or GPU MEX (profiling generated GPU code is the entry point to this skill's diagnostic-fix workflow)
• User wants to reduce GPU memory, improve kernel parallelism, or fix Performance Analyzer diagnostics
• User has a .m design file and representative inputs

When NOT to Use

• Workflows with no codegen
• coder.gpuConfig("exe"), standalone executables cannot be benchmarked or equivalence-checked from MATLAB. Suggest the user switch to mex or lib/dll and regenerate exe from the final optimized source.
• Simulink GPU code generation
• Writing new MATLAB functions from scratch (this skill optimizes existing code)
• Optimizing helper functions called by the design file, this skill optimizes the main design file only
• Hardware setup or CUDA toolkit installation
• General MATLAB performance tuning without GPU involvement
• The user's prompt does not mention GPU, codegen, CUDA, MEX, or profiling. Activation must be driven by the user's prompt alone, do not infer GPU intent from filenames or function contents. If unsure, ask before activating.

Workflow

Setup, Create Session Directory

All codegen artifacts, profiling outputs, and optimized versions go in a single temp directory for the entire session. pwd must be sessionDir for every codegen/benchmark/PA call, otherwise compiled MEX and SIL binaries land in the user's working directory.