Parallel Computing with MATLAB and Simulink

Perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and clouds

Solve computationally and data intensive problems using multicore processors, GPUs, and compute clusters. You can:

  • Leverage all compute resources
  • Parallelize MATLAB® applications using high-level constructs
  • Use NVIDIA® GPUs directly from MATLAB
  • Run multiple Simulink® simulations in parallel
  • Prototype on the desktop and scale to clusters and clouds

“Using Parallel Computing Toolbox we added four lines of code and wrote some simple task management scripts. Simulations that took months now run in a few days. MathWorks parallel computing tools enabled us to capitalize on the computing power of large clusters without a tremendous learning curve.”

Diglio Simoni, RTI

Using Parallel Computing with MATLAB and Simulink 

Desktop Parallel Computing for CPU and GPU

Parallel Computing Toolbox™ lets you take control of your local multicore processors and GPUs to speed up your work. High-level constructs enable you to parallelize MATLAB applications without CUDA® or MPI programming and run multiple Simulink simulations in parallel. Several MATLAB and Simulink products let you take advantage of your compute resources by setting a flag or preference. You can use the full processing power of multicore desktops by executing applications on workers (MATLAB computational engines) that run locally using Parallel Computing Toolbox. Prototype your applications and simulations on the desktop and then scale to clusters and clouds using MATLAB Parallel Server™ without recoding.

Scale MATLAB and Simulink on Clusters and in the Cloud

MATLAB Parallel Server enables you to scale MATLAB programs and Simulink simulations to clusters and clouds. You can develop and prototype your programs and simulations on your desktop with Parallel Computing Toolbox and then run them on clusters and clouds without recoding. MATLAB Parallel Server runs your programs and simulations as scheduled applications on your cluster using the MATLAB optimized scheduler provided by MATLAB Parallel Server or your own scheduler.