Parallel Computing Programming with Parallel Computing Toolbox and Distributed Computing Engine

This example demonstrates parallel computing programming using MATLAB 2007 and later versions with the Parallel Computing Toolbox and Distributed Computing Engine. The code provides substantial reference value and aligns with the official user guide documentation. The implementation showcases the Distributed Computing Engine's capability to distribute computational tasks across multiple computers, significantly enhancing processing speed and efficiency. The example includes practical approaches for utilizing the Parallel Computing Toolbox to develop parallel code that optimally leverages multi-core processors. Key programming aspects covered include: using parfor loops for parallel iterations, creating distributed arrays with distributed() function, and implementing spmd (Single Program Multiple Data) constructs for task parallelism. Additionally, we provide detailed configuration guidelines for setting up the parallel computing environment in MATLAB, including cluster profile management through parallel.cluster.Profiles and worker pool initialization using parpool(). The configuration section explains how to optimize resource allocation and manage distributed data storage to maximize parallel computing performance.