Accelerating the pace of engineering and science

MATLAB Distributed Computing Server

Backwards Compatibility

Backwards Compatibility

Upgrade your MATLAB Job Scheduler clusters and continue to use the previous release of Parallel Computing Toolbox

Cluster Profile Validation

Cluster Profile Validation

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

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

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

Support for Distributed Arrays

Support for Distributed Arrays

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

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

Third Party Scheduler Integration

Third Party Scheduler Integration

Obtain integration scripts for Third Party Schedulers (IBM Platform LSF, Grid Engine, PBS and SLURM) from MATLAB Central File Exchange instead of Parallel Computing Toolbox

Latest Releases

R2016b (Version 6.9) - 14 Sep 2016

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

  • Cluster Profile Validation: Choose which validation stages run and the number of MATLAB workers to use
  • Parallel Support for Tall Arrays: Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters
  • 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

See the Release Notes for details.

R2016a (Version 6.8) - 3 Mar 2016

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

  • Support for Distributed Arrays: Use enhanced distributed array functions including sparse input to direct (mldivide) and iterative solvers (cgs and pcg)
  • 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
  • Third Party Scheduler Integration: Obtain integration scripts for Third Party Schedulers (IBM Platform LSF, Grid Engine, PBS and SLURM) from MATLAB Central File Exchange instead of Parallel Computing Toolbox

See the Release Notes for details.

R2015b (Version 6.7) - 3 Sep 2015

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

  • Scheduler integration scripts for SLURM
  • Improved performance of mapreduce on Hadoop 2 clusters
  • parallel.pool.Constant function to create constant data on parallel pool workers, accessible within parallel language constructs such as parfor and parfeval

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

See the Release Notes for details.

R2014b (Version 6.5) - 2 Oct 2014

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

  • Data Analysis on Hadoop clusters using mapreduce
  • Additional MATLAB functions for distributed arrays, including fft2, fftn, ifft2, ifftn, cummax, cummin, and diff

See the Release Notes for details.