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.

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.

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.

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.

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.