function result = processData(data) result = mean(data) * 2; end
5. Parallel Computing for High-Performance Applications
For large simulations, MATLAB's Parallel Computing Toolbox allows you to leverage multi-core processors:
parfor loops -- Distribute iterations across multiple cores.
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gpuArray -- Accelerates computations using GPUs.
Parallel Pool -- Manages parallel workers efficiently.
parfor i = 1:100 results(i) = heavyComputation(i); end
This reduces computation time, making it ideal for AI, machine learning, and signal processing applications.
6. Integrating MATLAB with Python and Other Languages
MATLAB is powerful, but integrating it with Python, C++, or Java expands its capabilities:
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Python-MATLAB Bridge -- Run Python scripts within MATLAB using py. functions.
MEX Functions -- Execute C/C++ code directly in MATLAB for speed optimization.
Java Integration -- Use Java libraries for GUI development and enterprise applications.
