Solving Large Problems with MATLAB
University of Bristol
I wrote and debugged my program by using multiple MATLAB workers on a workstation. I then ran it on the EGEE Grid and reduced computation time from 5 days to just 6 hours.
- Parallel Computing Overview
- Solving Large Problems with MATLAB
- Optimizing Simulation Performance in Simulink
- Managing Parallel Computing Products in Your IT Environment
MathWorks parallel computing products help you harness high-performance computing resources. You can accelerate the processing of repetitive computations, process large amounts of data, or offload processor-intensive tasks on a computing resource of your choice—either multicore computers equipped with GPUs or larger resources such as computer clusters and grid and cloud computing services.
Exploit Computing Resources with Minimal Programming Effort
Built-in parallel computing capabilities in MathWorks products let you take advantage of your multiprocessing systems without programming effort. In addition, parallel programming constructs in Parallel Computing Toolbox™ such as parallel for-loops, distributed arrays, and GPU-enabled MATLAB functions offer an easy way to turn MATLAB applications into parallel MATLAB applications.
Scale up to Clusters and to Grid and Cloud Computing Services Without Reprogramming
Parallel MATLAB applications run without modifications on a variety of computing resources. You can start work on your multicore desktop using Parallel Computing Toolbox and, when you need more computing power, move to a computer cluster or a grid or cloud computing service running MATLAB Distributed Computing Server™.
Free Parallel Computing Interactive Kit
See how to solve large problems with minimal effort and reduce simulation time.
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