The Parallel Computing Toolbox (PCT) is a MATLAB toolbox. It lets you solve computationally intensive and data-intensive problems using MATLAB more quickly — on your local multicore computer or on RCS‘s Shared Computing Cluster. Parallel processing operations such as parallel for-loops and message-passing functions let you implement task- and data-parallel algorithms in MATLAB. Converting serial MATLAB applications to parallel MATLAB applications generally requires few code modifications and no programming in a low-level language is necessary. You can run your parallel applications interactively or in batch.
If your MATLAB application is to run multiple independent tasks (such as parametric studies of an analysis, image processing, or Monte Carlo simulations), you may not need to use the PCT. See the Running Multiple MATLAB Tasks page for details.
For some codes, it may be easier and more effective to exploit multithreading on shared-memory multicore processors than to use the PCT. On the one hand, it requires no parallel programming effort; on the other, it cannot be activated explicitly (e.g., with a command). Hence, multithreading is also referred to as “implicit parallelism.”
For details, consult the MATLAB Parallel Computing Toolbox User’s Guide (PDF file).
- Where To Run The PCT
- What It Takes To Parallelize Code
- Parallel Environments
- Types Of Parallel Jobs
- Task Parallel Jobs
- Data Parallel Jobs
- Data Parallel Examples: Numerical Integration
- Array Distribution
- Working with Distributed Arrays
- Code Parallelization Procedures
- Parallel Scalability
- Implicit Parallelism
- How To Run Batch Jobs On The Shared Computing Cluster
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