This release of MATLAB® products no longer supports 32-bit Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™ on Windows® operating systems.
You can no longer install the parallel computing products on 32-bit Windows operating systems. If you must use Windows operating systems for the parallel computing products, upgrade to 64-bit MATLAB products on a 64-bit operating system.
This release offers a new set of scripts containing submit and decode functions to support Simple Linux® Utility for Resource Management (SLURM), using the generic scheduler interface. The pertinent code files are in the folder:
matlabroot/toolbox/distcomp/examples/integration/slurm
where matlabroot is your installation
location.
The slurm folder contains a README file
of instructions, and folders for shared, nonshared,
and remoteSubmission network configurations.
For more information, view the files in the appropriate folders. See also Program Independent Jobs for a Generic Scheduler and Program Communicating Jobs for a Generic Scheduler.
mapreduce on Hadoop 2 clustersThe performance of mapreduce running on
a Hadoop® 2.x cluster with MATLAB Distributed Computing Server is
improved in this release for large input data.
parallel.pool.Constant function to create constant data on parallel pool workers, accessible within parallel
language constructs such as parfor and parfevalA new parallel.pool.Constant function allows
you to define a constant whose value can be accessed by multiple parfor-loops
or other parallel language constructs (e.g., spmd or parfeval)
without the need to transfer the data multiple times.
For more information and examples, see parallel.pool.Constant.
This version of MATLAB Distributed Computing Server software is accompanied by a corresponding new version of Parallel Computing Toolbox software.
As with every new release, if you are using both parallel computing products, you must upgrade Parallel Computing Toolbox and MATLAB Distributed Computing Server together. These products must be the same version to interact properly with each other.
Jobs created in one version of Parallel Computing Toolbox software
will not run in a different version of MATLAB Distributed Computing Server software,
and might not be readable in different versions of the toolbox software.
The job data stored in the folder identified by JobStorageLocation (formerly DataLocation)
might not be compatible between different versions of MATLAB Distributed Computing Server.
Therefore, JobStorageLocation should not be shared
by parallel computing products running different versions, and each
version on your cluster should have its own JobStorageLocation.
You can now run parallel mapreduce on
any cluster that supports a parallel pool.
In addition to multicast, the discover cluster functionality of Parallel Computing Toolbox can now use DNS to locate MATLAB job scheduler (MJS) clusters. For information about cluster discovery, see Discover Clusters. For information about configuring and verifying the required DNS SRV record on your network, see DNS SRV Record.
On 64-bit Windows platforms, Microsoft® MPI (MS-MPI) is now the default MPI implementation for local clusters on the client machine.
For MATLAB job scheduler (MJS) clusters on Windows platforms,
you can use MS-MPI by specifying the -useMSMPI flag
with the startjobmanager command.
mdce_def file The following parameters are new to the mdce_def file
for controlling the behavior of MATLAB job scheduler (MJS) clusters.
ALL_SERVER_SOCKETS_IN_CLUSTER —
This parameter controls whether all client connections are outbound,
or if inbound connections are also allowed.
JOBMANAGER_PEERSESSION_MIN_PORT, JOBMANAGER_PEERSESSION_MAX_PORT —
These parameters set the range of ports to use when ALL_SERVER_SOCKETS_IN_CLUSTER = true.
WORKER_PARALLELPOOL_MIN_PORT, WORKER_PARALLELPOOL_MAX_PORT —
These parameters set the range of ports to use on worker machines
for parallel pools.
For more information and default settings for these parameters,
see the appropriate mdce_def file for your platform:
matlabroot\toolbox\distcomp\bin\mdce_def.bat
matlabroot/toolbox/distcomp/bin/mdce_def.sh
By default in this release, ALL_SERVER_SOCKETS_IN_CLUSTER is true,
which makes all connections outbound from the client. For pre-R2015a
behavior, set its value to false, which also initiates
a set of inbound connections to the client from the MJS and workers.
This release no longer supports GPU devices on 32-bit Windows machines.
GPU devices on 32-bit Windows machines are not supported in this release. Instead, use GPU devices on 64-bit machines.
In a future release, support will be removed for Parallel Computing Toolbox and MATLAB Distributed Computing Server on 32-bit Windows machines.
Parallel Computing Toolbox and MATLAB Distributed Computing Server are still supported on 32-bit Windows machines in this release, but parallel language commands can generate a warning. In a future release, support will be completely removed for these computers, at which time it will not be possible to install the parallel computing products on them.
mapreduceMATLAB Distributed Computing Server supports the use of Hadoop clusters
for the execution environment of mapreduce applications.
For more information, see:
fft2, fftn, ifft2, ifftn, cummax, cummin, and diffThe following functions now support distributed arrays with all forms of codistributor (1-D and 2DBC), or are enhanced in their support for this release:
besselh besseli besselj besselk bessely beta betainc betaincinv betaln cart2pol cart2sph compan corrcoef cov cummax cummin diff | erf erfc erfcinv erfcx erfinv fft2 fftn gamma gammainc gammaincinv gammaln hankel hsv2rgb ifft ifft2 ifftn isfloat | isinteger islogical isnumeric median mode pol2cart psi rgb2hsv sph2cart std toeplitz trapz unwrap vander var |
For a list of MATLAB functions that support distributed arrays, see MATLAB Functions on Distributed and Codistributed Arrays.
You can now duplicate job objects, allowing you to resubmit jobs that had finished or failed.
The syntax to duplicate a job is
newjob = recreate(oldjob)
where oldjob is an existing job object. The newjob object
has all the same tasks and settable properties as oldjob,
but receives a new ID. The old job can be in any
state; the new job state is pending.
You can also specify which tasks from an existing independent job to include in the new job, based on the task IDs. For example:
newjob = recreate(oldjob,'TaskID',[33:48]);For more information, see the recreate reference
page.
The following functions now support distributed arrays with all forms of codistributor (1-D and 2DBC), or are enhanced in their support for this release:
eye ifft randi rand randn |
Note the following enhancements for some of these functions:
For a list of MATLAB functions that support distributed arrays, see MATLAB Functions on Distributed and Codistributed Arrays.
The programming interface characterized by distributed jobs
and parallel jobs has been removed. This old interface used functions
such as findResource, createParallelJob, getAllOutputArguments, dfeval,
etc.
The functions of the old programming interface now generate errors. You must migrate your code to the interface described in the R2012a Parallel Computing Toolbox release topic New Programming Interface.
matlabpool Function Being Removed The matlabpool function is being removed.
Calling matlabpool continues to work in this
release, but now generates a warning. You should instead use parpool to
create a parallel pool.
parpool: New command-line interface (replaces matlabpool), desktop indicator, and preferences for easier interaction with a parallel
pool of MATLAB workers This release introduces a number of enhancements for interacting with parallel pool resources. For more detailed descriptions of these enhancements, see parpool: New command-line interface (replaces matlabpool), desktop indicator, and preferences for easier interaction with a parallel pool of MATLAB workers in the Parallel Computing Toolbox release notes.
Parallel pool syntax replaces MATLAB pool syntax for
executing parallel language constructs such as parfor, spmd, Composite,
and distributed. The pool is represented in MATLAB
by a parallel.Pool object.
A new icon at the lower-left corner of the desktop indicates the current pool status. Icon color and tool tips let you know if the pool is busy or ready, how large it is, and when it might time out. You can click the icon to start a pool, stop a pool, or access your parallel preferences.
Your MATLAB preferences now include a group of settings
for parallel preferences. These settings control general behavior
of clusters and parallel pools for your MATLAB session. You can access
your parallel preferences in the MATLAB toolstrip, from the parallel
pool status icon, or by typing preferences at the
command line.
For more information, see Parallel Preferences.
This release continues to support MATLAB pool language usage, but this support might discontinue in future releases. You should update your code as soon as possible to use parallel pool syntax instead.
parfor or spmd You can set your parallel preferences so that a parallel pool
automatically starts whenever you execute a language construct that
runs on a pool, such as parfor, spmd, Composite, distributed, parfeval,
and parfevalOnAll.
The default preference setting is to automatically start a pool
when a parallel language construct requires it. If you want to make
sure a pool does not start automatically, you must change your parallel
preference setting. You can also work around this by making sure to
explicitly start a pool with parpool before encountering
any code that needs a pool.
By default, a parallel pool will shut down if idle for 30 minutes. To prevent this, change the setting in your parallel preferences; or the pool indicator tool tip warns of an impending timeout and provides a link to extend it.
You now have the option to start a parallel pool on a local
or MJS cluster so that the pool does not support running SPMD constructs.
This allows the parallel pool to keep running even if one or more
workers aborts during parfor execution. You explicitly
disable SPMD support when starting the parallel pool by setting its 'SpmdEnabled' property false in
the call to the parpool function. For example:
p = parpool('SpmdEnabled',false);Running any code (including MathWorks toolbox code) that uses SPMD constructs, on a parallel pool that was created without SPMD support, will generate errors.
permute, ipermute, and sortrowsThe following functions now support distributed arrays with all forms of codistributor (1-D and 2DBC), or are enhanced in their support for this release:
ipermute permute sortrows cast | zeros ones nan inf true false |
For more information about these functions and distributed arrays, see More MATLAB functions enabled for distributed arrays: permute, ipermute, and sortrows in the Parallel Computing Toolbox release notes.
The parallel computing products are now shipping MPICH2 version 1.4.1p1 on all platforms.
If you use your own MPI builds, you might need to create new builds compatible with this latest version, as described in Use Different MPI Builds on UNIX Systems.
Parallel Computing Toolbox can now automatically attach files
to a job so that workers have the necessary code files for evaluating
tasks. When you set a job object's AutoAttachFiles to
true, an analysis determines what files on the client machine are
necessary for the evaluation of your job, and those files are automatically
attached to the job and sent to the worker machines.
For more information, see Automatic detection and transfer of files required for execution in both batch and interactive workflows in the Parallel Computing Toolbox release notes.
When multiple workers run on a single compute node with multiple
GPU devices, the devices are automatically divided up among the workers.
If there are more workers than GPU devices on the node, multiple workers
share the same GPU device. If you put a GPU device in 'exclusive' mode,
only one worker uses that device. As in previous releases, you can
change the device used by any particular worker with the gpuDevice function.
You can let MATLAB discover clusters for you. Use either of the following techniques to discover those clusters which are available for you to use:
On the Home tab in the Environment section, click Parallel > Discover Clusters.
In the Cluster Profile Manager, click Discover Clusters.
For more information, see Discover Clusters.
| Release | Features or Changes with Compatibility Considerations |
|---|---|
| R2015b | |
| R2015a | |
| R2014b | None |
| R2014a | |
| R2013b |
|
| R2013a | None |
| R2012b | None |