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.
Run loop iterations in parallel on a parallel pool
using the parfor language construct
Offload execution of a function or script to run in a cluster or desktop background
Transfer data between MATLAB and a graphics processing unit (GPU); run code on a GPU
Partition arrays across multiple MATLAB workers for data-parallel computing and simultaneous execution
Accelerate mapreduce and datastore programs by running on a parallel pool or Hadoop® cluster
Identify cluster resources and specify usage parameters
Program jobs and tasks with more detailed control
Improve performance of parallel code