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Parallel Computing Toolbox

Parallel Support for Tall Arrays

Parallel Support for Tall Arrays

Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters

Support for GPU arrays

Support for GPU arrays

Use enhanced gpuArray functions including new sparse iterative solver bicg

Parallel Menu Enhancement

Parallel Menu Enhancement

Use the new menu items in the Parallel Menu to configure and manage cloud based resources

Support for New Data Types in Distributed Arrays

Support for New Data Types in Distributed Arrays

Use enhanced functions for creating distributed arrays of: datetimedurationcalendarDurationstring;categorical; and table

Loading Distributed Arrays

Loading Distributed Arrays

Load distributed arrays in parallel using datastore

Cluster Profile Validation

Cluster Profile Validation

Choose which validation stages run and the number of MATLAB workers to use

GPU Support for Sparse Matrices

GPU Support for Sparse Matrices

Use enhanced gpuArray functions for sparse matrices on GPUs

Support for Distributed Arrays

Support for Distributed Arrays

Use enhanced distributed array functions including sparse input to direct (mldivide) and iterative solvers (cgs and pcg)

GPU-Accelerated Deep Learning

GPU-Accelerated Deep Learning

Use Neural Network Toolbox to train deep convolutional neural networks with GPU-enabled acceleration for image classification tasks

GPU-enabled MATLAB Functions

GPU-enabled MATLAB Functions

Accelerate applications using GPU-enabled MATLAB functions for linear equations, descriptive statistics and set operations

Parallel-Enabled Gradient Estimation

Parallel-Enabled Gradient Estimation

Accelerate more nonlinear solvers in the Optimization Toolbox with parallel finite difference estimation of gradients and Jacobians

Hadoop Kerberos Support

Hadoop Kerberos Support

Improved support for Hadoop in a Kerberos authenticated environment

Increased Data Transfer Limits

Increased Data Transfer Limits

Transfer data up to 4GB in size between client and workers in any job using a MATLAB job scheduler cluster

Latest Releases

R2016b (Version 6.9) - 14 Sep 2016

Version 6.9, part of Release 2016b, includes the following enhancements:

  • Parallel Support for Tall Arrays: Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters
  • Support for GPU arrays: Use enhanced gpuArray functions, including new sparse iterative solver bicg
  • Parallel Menu Enhancement: Use the new menu items in the Parallel Menu to configure and manage cloud based resources
  • New Data Types in Distributed Arrays: Use enhanced functions for creating distributed arrays of: datetime; duration; calendarDuration; string; categorical; and table
  • Loading Distributed Arrays: Load distributed arrays in parallel using datastore
  • Cluster Profile Validation: Choose which validation stages run and the number of MATLAB workers to use

See the Release Notes for details.

R2016a (Version 6.8) - 3 Mar 2016

Version 6.8, part of Release 2016a, includes the following enhancements:

  • GPU Support for Sparse Matrices: Use enhanced gpuArray functions for sparse matrices on GPUs
  • Support for Distributed Arrays: Use enhanced distributed array functions including sparse input to direct (mldivide) and iterative solvers (cgs and pcg)
  • GPU-Accelerated Deep Learning: Use Neural Network Toolbox to train deep convolutional neural networks with GPU-enabled acceleration for image classification tasks
  • GPU-enabled MATLAB Functions: Accelerate applications using GPU-enabled MATLAB functions for linear equations, descriptive statistics and set operations
  • Parallel-Enabled Gradient Estimation: Accelerate more nonlinear solvers in the Optimization Toolbox with parallel finite difference estimation of gradients and Jacobians
  • Hadoop Kerberos Support: Improved support for Hadoop in a Kerberos authenticated environment
  • Increased Data Transfer Limits: Transfer data up to 4GB in size between client and workers in any job using a MATLAB job scheduler cluster

See the Release Notes for details.

R2015b (Version 6.7) - 3 Sep 2015

Version 6.7, part of Release 2015b, includes the following enhancements:

  • More than 90 GPU-enabled functions in Statistics and Machine Learning Toolbox, including probability distribution, descriptive statistics, and hypothesis testing
  • Additional GPU-enabled MATLAB functions, including support for sparse matrices
  • mexcuda function for easier compilation of MEX-files containing CUDA code
  • Scheduler integration scripts for SLURM
  • parallel.pool.Constant function to create constant data on parallel pool workers, accessible within parallel language constructs such as parfor and parfeval
  • Improved performance of mapreduce on Hadoop 2 clusters

See the Release Notes for details.

R2015a (Version 6.6) - 5 Mar 2015

Version 6.6, part of Release 2015a, includes the following enhancements:

  • Support for mapreduce function on any cluster that supports parallel pools
  • Sparse arrays with GPU-enabled functions
  • Additional GPU-enabled MATLAB functions
  • pagefun support for mrdivide and inv functions on GPUs
  • Enhancements to GPU-enabled linear algebra functions
  • Parallel data reads from a datastore with MATLAB partition function

See the Release Notes for details.

R2014b (Version 6.5) - 2 Oct 2014

Version 6.5, part of Release 2014b, includes the following enhancements:

  • Parallelization of mapreduce on local workers
  • Additional GPU-enabled MATLAB functions, including accumarray, histc, cummax, and cummin
  • pagefun support for mldivide on GPUs
  • Additional MATLAB functions for distributed arrays, including fft2, fftn, ifft2, ifftn, cummax, cummin, and diff

See the Release Notes for details.