Optimize MATLAB for GPU Code Generation

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.

Two helpers this skill calls — benchmarkMex and extractDiagnostics — live in the scripts/ subfolder of this skill (the folder containing this