Parallel Computing Toolbox
Product Description
- Parallel Computing Toolbox 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
Programming Parallel Applications
Parallel Computing Toolbox provides several high-level programming constructs that let you convert your applications to take advantage of computers equipped with multicore processors and GPUs. Constructs such as parallel for-loops (parfor) and special array types for distributed processing and for GPU computing simplify parallel code development by abstracting away the complexity of managing computations and data between your MATLAB session and the computing resource you are using.
You can run the same application on a variety of computing resources without reprogramming it. The parallel constructs function in the same way, regardless of the resource on which your application runs—a multicore desktop (using the toolbox) or on a larger resource such as a computer cluster (using toolbox with MATLAB Distributed Computing Server).
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