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Parallel Computing Toolbox
Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
The toolbox lets you use the full processing power of multicore desktops by executing applications on workers (MATLAB computational engines) that run locally. Without changing the code, you can run the same applications on clusters or clouds (using MATLAB Parallel Server™). You can also use the toolbox with MATLAB Parallel Server to execute matrix calculations that are too large to fit into the memory of a single machine.
Getting Started
Learn the basics of Parallel Computing Toolbox
Parallel Computing Fundamentals
Choose a parallel computing solution
Parallel for-Loops (parfor)
Use parallel processing by running parfor on
workers in a parallel pool
Asynchronous Parallel Programming
Evaluate functions in the background using parfeval
Big Data Processing
Analyze big data sets in parallel using distributed
arrays, tall arrays, datastores, or mapreduce,
on Spark® and Hadoop® clusters
Batch Processing
Offload execution of functions to run in the background
GPU Computing
Accelerate your code by running it on a GPU
Clusters and Clouds
Discover cluster resources, and work with cluster profiles.
Performance Profiling
Improve performance of parallel code
