Parallel Computing Toolbox

Introduction

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 provides twelve workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.

Key Features

• Parallel for-loops (parfor) for running task-parallel algorithms on multiple processors
• Support for CUDA-enabled NVIDIA GPUs
• Ability to run twelve workers locally on a multicore desktop
• Computer cluster and grid support (with MATLAB Distributed Computing Server)
• Interactive and batch execution of parallel applications
• Distributed arrays and single program multiple data (spmd) construct for large dataset handling and data-parallel algorithms

Parallel computing with MATLAB. You can use Parallel Computing Toolbox to run applications on a multicore desktop with twelve workers available in the toolbox, take advantage of GPUs, and scale up to a cluster (with MATLAB Distributed Computing Server).