Parallel Computing Toolbox
Description
- Introduction and Key Features
- Programming Parallel Applications
- Using Built-In Parallel Algorithms in Other MathWorks Products
- Speeding Up Task-Parallel Applications
- Speeding Up MATLAB Computations with GPUs
- Scaling Up to Clusters, Grids, and Clouds Using MATLAB Distributed Computing Server
- Implementing Data-Parallel Applications using the Toolbox and MATLAB Distributed Computing Server
- Running Parallel Applications Interactively and as Batch Jobs
- Parallel Computer 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).
