MATLAB Performance Optimization Workflow

MATLAB Performance Optimization Workflow

Systematic 7-step workflow for finding and fixing performance bottlenecks in MATLAB code.

When to Use

• User asks to speed up or optimize MATLAB code
• User wants to find why their MATLAB code is slow
• User has a function or script that takes too long to run
• User asks to benchmark or time MATLAB code
• User wants to compare performance before and after a change
• User asks about MATLAB performance best practices

When NOT to Use

• Optimizing Simulink model simulation speed (use Simulink Profiler)
• The bottleneck is in compiled C/MEX code that can't be changed at the M-code level
• The performance issue is purely I/O-bound (file reads, network, database)
• User wants to write performance tests (use the writing-matlab-perf-tests skill)

The 7-Step Workflow

Step 1: Establish Baseline

Measure current performance so you have a number to improve against.

For a single function:

f = @() targetFunction(input1, input2);
baseline = timeit(f);
fprintf('Baseline: %.4f s\n', baseline);

For GPU code:

f = @() gpuFunction(gpuInput);
baseline = gputimeit(f);

For a script or multi-step workflow: