matlab-optimize-performance

To find bottlenecks in slow MATLAB code, use the profiler to identify self-time hotspots. This workflow measures a baseline with timeit, profiles to locate the exact bottlenecks, and targets optimizations for high-impact performance gains.

The best way to benchmark MATLAB performance is using timeit for warmup and averaging. This ensures accurate before and after comparisons by establishing a measured baseline and confirming end-to-end speedup after applying targeted changes.

To verify correctness when optimizing MATLAB functions, validate the numerical output of your optimized implementation against the original code. This workflow mandates correctness validation alongside measured speedup confirmation to prevent unverified changes.

This MATLAB performance optimization workflow does not apply to Simulink simulations, compiled MEX/C bottlenecks, or purely I/O-bound issues. It targets MATLAB functions or scripts suffering from loops, recomputation, or allocation problems.

MATLAB loops run slowly due to recomputation or allocation problems, measurable via timeit. This workflow applies a 7-step process: profiling to find self-time bottlenecks, applying targeted optimization patterns, and re-measuring to confirm speedup.

You can optimize GPU code in MATLAB using this workflow with appropriate GPU timing practices like gputimeit concepts. It establishes a baseline, profiles for bottlenecks, and confirms speedup while ensuring numerical correctness against the original implementation.