Which processor is good for running matlab?

Parallel computing

 

Parallel computing with MATLAB and Simulink

Perform extensive calculations on multi-core computers, GPUs, in clusters and clouds

Solve computational and data-intensive problems using multi-core processors, GPUs, and computer clusters. You can:

  • use all available computing power
  • MATLAB® Applications using High-level constructs parallelize
  • NVIDIA® Graphics processors use it directly in MATLAB
  • several Simulink®- Run simulations in parallel
  • on the desktop prototype and in clusters and clouds scale

“With the Parallel Computing Toolbox, we only had to add four lines of code and write some simple administrative scripts. Simulations that used to take months now only take a few days. Thanks to the MathWorks tools for parallel processing, we were able to benefit from the computing power of large clusters without extensive training. "

Diglio Simoni, RTI

Parallel computing on the desktop for CPU and GPU

With the Parallel Computing Toolbox ™, you take control of local multi-core processors and graphics processors to get your work done faster. High-level constructs enable you to parallelize MATLAB applications without CUDA®- or MPI programming and the parallel execution of several Simulink simulations. Various MATLAB and Simulink products support your processing resources by allowing you to set a label or preference. With the Parallel Computing Toolbox you can exploit the full computing power of multi-core computers by running applications on locally operated workers (MATLAB processes). Without changing the code, applications can be prototyped and then scaled in clusters or clouds with MATLAB Parallel Server ™.

Scale MATLAB and Simulink in clusters and the cloud

With MATLAB Parallel Server, you can scale MATLAB programs and Simulink simulations in clusters and clouds. With the Parallel Computing Toolbox, programs and simulations can be developed and prototyped on the desktop in order to then run them in clusters and clouds without code changes. Using a scheduler (either the built-in, optimized MATLAB scheduler or your own), MATLAB Parallel Server runs your programs and simulations on a schedule in your cluster.

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