MATLAB Memory Optimization Workflow

MATLAB Memory Optimization Workflow

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

When to Use

• User gets out-of-memory errors running MATLAB code
• User wants to reduce memory usage of their MATLAB program
• User wants to process larger datasets without running out of memory
• User asks to profile or measure memory allocations

When NOT to Use

• The bottleneck is execution speed, not memory (use matlab-optimize-performance)
• The memory issue is in compiled C/MEX code that can't be changed at the M-code level
• Memory usage is dominated by I/O buffers (memory-mapped files, database connections)

The 7-Step Workflow

Step 1: Establish Memory Baseline

Measure current memory usage before making changes.

m0 = memory;
targetFunction(inputs);
m1 = memory;
deltaBytes = m1.MemUsedMATLAB - m0.MemUsedMATLAB;
fprintf('Memory delta: %.2f MB\n', deltaBytes / 1e6);

When memory errors (Linux/macOS), use whos for variable sizes or Java runtime for heap:

info = whos('result');
fprintf('Variable size: %.2f MB\n', info.bytes / 1e6);

Step 2: Profile Memory Allocations

Find where memory is being allocated.