Parallel Computing Toolbox

Perform parallel computations on multicore computers, GPUs, and computer clusters

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.

Parallel for-Loops (parfor)

Run loop iterations in parallel on a parallel pool using the parfor language construct

Batch Processing

Offload execution of a function or script to run in a cluster or desktop background

GPU Computing

Transfer data between MATLAB and a graphics processing unit (GPU); run code on a GPU

Distributed Arrays and SPMD

Partition arrays across multiple MATLAB workers for data-parallel computing and simultaneous execution

Big Data

Accelerate mapreduce and datastore programs by running on a parallel pool or Hadoop® cluster

Cluster Profiles and Computation Scaling

Identify cluster resources and specify usage parameters

Detailed Job and Task Control

Program jobs and tasks with more detailed control


Improve performance of parallel code