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—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.
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 a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.
Learn the basics of Parallel Computing Toolbox
Choose a parallel computing solution
Use parallel processing by running parfor on
workers in a parallel pool
Evaluate functions in the background using parfeval
Analyze big data sets in parallel using distributed
arrays, tall arrays, datastores, or mapreduce,
on Spark® and Hadoop® clusters
Offload execution of functions to run in the background
Accelerate your code by running it on a GPU
Discover cluster resources and work with cluster profiles
Improve performance of parallel code