What problem does it solve?
MATLAB programs frequently hit out-of-memory errors or consume excessive RAM when processing large datasets, and developers often guess at fixes instead of measuring. This Skill provides a structured measure-profile-optimize-verify workflow that finds real memory bottlenecks and applies proven MATLAB-specific optimization patterns.
Core Features & Use Cases
- Memory Baseline and Profiling: Measure memory deltas with
memory, whos, and profile -memory, with platform-specific fallbacks for Linux and macOS.
- Optimization Pattern Library: Apply nine documented patterns including cell collection with
vertcat, implicit expansion over repmat, copy-on-write sharing, and sparse matrix conversion.
- Correctness Verification: Assert that optimized code produces numerically identical results before reporting reduction percentages.
- Use Case: A user processing large sensor datasets gets out-of-memory errors. The Skill profiles the code, finds an array growing in a loop, replaces it with cell collection plus
vertcat, and verifies a measurable memory reduction with identical output.
Quick Start
Ask the agent to profile my MATLAB script for memory bottlenecks and reduce its memory usage while verifying the results stay identical.
