Parallel Computing Toolbox 4.0

Product Description

• Parallel Computing Toolbox Key Features
• Programming Parallel Applications in MATLAB
• Working in an Interactive Parallel Environment
• Working in Batch Environments
• Scaling to a Cluster Using MATLAB Distributed Computing Server

Introduction

Parallel Computing Toolbox lets you solve computationally and data-intensive problems using MATLAB and Simulink on multicore and multiprocessor computers. Parallel processing constructs such as parallel for-loops and code blocks, distributed arrays, parallel numerical algorithms, and message-passing functions let you implement task- and data-parallel algorithms in MATLAB at a high level without programming for specific hardware and network architectures. As a result, converting serial MATLAB applications to parallel MATLAB applications requires few code modifications and no programming in a low-level language. You can run your applications interactively or offline, in batch environments.

Developing parallel applications with Parallel Computing Toolbox. The toolbox enables application prototyping on the desktop with up to four local workers (left), and, with MATLAB Distributed Computing Server (right), applications can be scaled to multiple computers on a cluster. Click on image to see enlarged view.

You can use the toolbox to execute applications on a single multicore or multiprocessor desktop. Without changing the code, you can run the same application on a computer cluster (using MATLAB Distributed Computing Server™). Parallel MATLAB applications can be distributed as executables or shared libraries (built using MATLAB Compiler™) that can access MATLAB Distributed Computing Server.

Key Features

• Support for data-parallel and task-parallel application development
• Ability to annotate code segments with parfor (parallel for-loops) and spmd (single program multiple data) for implementing task- and data-parallel algorithms
• High-level constructs such as distributed arrays, parallel algorithms, and message-passing functions for processing large data sets on multiple processors
• Ability to run four workers locally on a multicore desktop
• Integration with MATLAB Distributed Computing Server for cluster-based applications that use any scheduler or any number of workers
• Interactive and batch execution modes