Matlab Optimize Performance

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:

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

For GPU code:

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

For a script or multi-step workflow:

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% Warmup run (first call includes JIT compilation)
myWorkflow(inputs);

% Timed run
tic;
myWorkflow(inputs);
baseline = toc;
fprintf('Baseline: %.4f s\n', baseline);

timeit is preferred because it handles warmup and runs multiple samples automatically.

Step 2: Profile and Analyze

Find where the time is actually spent. Do NOT guess — always profile.

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profile on;
targetFunction(input1, input2);
profile off;
profile viewer;

Reading profiler results:

1. Function summary — shows total time and self-time per function. Self-time is time spent in that function, not its callees. Start with the highest self-time.
2. Per-line detail — click a function name to see time spent on each line. This reveals the exact bottleneck lines.
3. Call count — functions called thousands/millions of times are prime optimization targets.

Tips:

• Run the profiled code multiple times (in a loop) if it's very fast, so the profiler collects enough samples
• Look at self-time, not total time, to find the true bottleneck
• Drill into functions — the summary page only tells part of the story

Step 3: Identify Optimization Opportunities

Based on profiling results, identify which patterns apply. Read references/optimization-patterns.md for the full catalog.

High-impact patterns:

Before optimizing, verify the target is worth it:

• Is self-time > 10% of total? If not, optimizing it won't matter much.
• Is it called in a tight loop? High call count × small time = big total.
• Is it M-code or a built-in? You can't make a built-in faster, but you can often call it fewer times (e.g., pass a matrix to filtfilt/filter instead of looping over columns).

Step 4: Implement Optimizations

Apply the patterns identified in Step 3. See references/optimization-patterns.md for the full catalog with before/after code examples.

General principles:

• Start with the highest-impact pattern from profiling
• Move invariant work out of loops (object creation, option parsing, constant expressions)
• Replace element-wise loops with array operations where possible
• Use purpose-built functions (discretize, cumsum, hypot) instead of hand-written equivalents
• For large data, batch the vectorization to control memory (see Pattern 9 in catalog)

Example — move invariant work out of loops:

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% Before: repeated expensive setup
for i = 1:n
    opts = optimoptions('fminunc', 'Display', 'off');
    result(i) = fminunc(@(x) cost(x, data(i)), x0, opts);
end

% After: setup once
opts = optimoptions('fminunc', 'Display', 'off');
for i = 1:n
    result(i) = fminunc(@(x) cost(x, data(i)), x0, opts);
end

Step 5: Measure Optimized Performance

Re-measure using the same method as Step 1:

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f = @() optimizedFunction(input1, input2);
optimized = timeit(f);
speedup = baseline / optimized;
fprintf('Optimized: %.4f s (%.2fx speedup)\n', optimized, speedup);

A speedup of 1.2x or more is considered significant. Below that, measurement noise makes it hard to be confident the change helped.

Step 6: Verify Correctness

Every optimization must produce the same results as the original:

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original = originalFunction(input1, input2);
fast = optimizedFunction(input1, input2);

% Numeric comparison (allows floating-point tolerance)
maxErr = max(abs(original(:) - fast(:)));
fprintf('Max error: %.2e\n', maxErr);
assert(maxErr < 1e-10, 'Results differ beyond tolerance!');

For non-numeric outputs:

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assert(isequal(original, fast), 'Results differ!');

If results differ slightly due to floating-point reordering (e.g., summing in a different order), that's usually acceptable. Document the expected tolerance.

Step 7: Report Results

Summarize what was done and the improvement achieved:

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fprintf('\n=== Performance Optimization Report ===\n');
fprintf('Target: %s\n', funcName);
fprintf('Baseline: %.4f s\n', baseline);
fprintf('Optimized: %.4f s\n', optimized);
fprintf('Speedup: %.2fx\n', speedup);
fprintf('Correctness: max error = %.2e\n', maxErr);
fprintf('Pattern applied: %s\n', patternName);

For multiple optimizations, report each speedup individually and the overall end-to-end improvement.

Key Rules

1. Always profile before optimizing — never guess where the bottleneck is
2. One change at a time — measure after each optimization to know what helped
3. Verify correctness — every optimization must produce equivalent output
4. 1.2x threshold — speedups below 1.2x are not reliably distinguishable from noise
5. GPU timing — always wait(gpuDevice) before and after timing GPU code
6. Use timeit — it handles warmup and averaging; avoid raw tic/toc for benchmarks

Common Mistakes

Reference Files

• references/optimization-patterns.md — Full catalog of optimization patterns with code examples and measured speedups
• references/measurement-templates.md — Ready-to-use MATLAB script templates for each workflow step

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