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Parallel Computing Toolbox

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Perform parallel computations on multicore computers, GPUs, and computer clusters

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Parallel computing with MATLAB. You can use Parallel Computing Toolbox to run applications on a multicore desktop with twelve workers available in the toolbox, take advantage of GPUs, and scale up to a cluster (with MATLAB Distributed Computing Server).

 

Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.

The toolbox provides twelve workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.

MATLAB GPU Support

MATLAB GPU Computing with NVIDIA CUDA-Enabled GPUs

MATLAB® GPU support is available in Parallel Computing Toolbox™. Using MATLAB for GPU computing lets you take advantage of GPUs without low-level C or Fortran programming.

MATLAB supports NVIDIA® CUDA™-enabled GPUs with compute capability version 1.3 or higher, such as Tesla™ 10-series and 20-series GPUs.

MATLAB CUDA support provides the base for GPU-accelerated MATLAB operations and lets you integrate your existing CUDA kernels into MATLAB applications.

MATLAB GPU computing capabilities include:

  • Data manipulation on NVIDIA GPUs
  • GPU-accelerated MATLAB operations
  • Integration of CUDA kernels into MATLAB applications without low-level C or Fortran programming
  • Use of multiple GPUs on the desktop (via the toolbox) and a computer cluster (via MATLAB Distributed Computing Server™)

 

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