| Version 3.3 (R2008a) Parallel Computing Toolbox™ Software Release Notes | |
This table summarizes what is new in Version 3.3 (R2008a):
| New Features and Changes | Version Compatibility Considerations | Fixed Bugs and Known Problems | Related Documentation at Web Site |
|---|---|---|---|
Yes | Yes — Details labeled as Compatibility Considerations, below. See also Summary. | Bug Reports | No |
New features and changes introduced in this version are
As of result of the product name changes, some function names are changing in this release.
Two function names are changed to correspond to the new product names:
dctconfig has been renamed pctconfig.
dctRunOnAll has been renamed pctRunOnAll.
The new batch function allows you to offload work from the client to one or more workers. The batch submission can run scripts that can include jobs that distribute work to other workers. For more information, see the batch reference page, and Getting Started in the Parallel Computing Toolbox User's Guide.
The batch functionality is implemented using the new MATLAB®pool job feature. A MATLAB pool job uses one worker to distribute a job to other workers, thereby freeing the client from the burden of tracking and job's progress and manipulating data. For more information, see the createMatlabPoolJob reference page.
The createJob and createParallelJob functions have been enhanced to run without requiring a scheduler object as an argument. This is also true for the new createMatlabPoolJob function. When a scheduler is not specified, the function uses the scheduler identified in the applicable parallel configuration. For details, see the reference page for each function.
The default size limitation on data transfers between clients and workers has been significantly increased. In previous releases the default limitation imposed by the JVM memory allocation was approximately 50 MB. The new higher limits are approximately 600 MB on 32-bit systems and 2 GB on 64-bit systems. See Object Data Size Limitations.
Several functions related to distributed arrays have changed names in this release.
The following table summarizes the changes in function names relating to distributed arrays.
| Old Function Name | New Function Name |
|---|---|
| darray | distributed, distributor |
| distribute | distributed |
| dcolonpartition | defaultPartition |
| distribdim | distributionDimension |
| isdarray | isdistributed |
| labgrid | labGrid |
| local | localPart |
| partition | distributionPartition |
| localspan | globalIndices |
Parallel Computing Toolbox™ software now fully supports PBS Pro® and TORQUE schedulers. These schedulers are integrated into parallel configurations and scheduler-related functions like findResource.
The findResouce function now sets the properties on the object it creates according to the configuration identified in the function call.
In past releases, findResource could use a configuration to identify a scheduler, but did not apply the configuration settings to the scheduler object properties. If your code uses separate statements to find an object then set properties, this still works, but is not necessary any more.
The parfor statement is now recognized only for parallel for-loops, not for loops over a distributed range in parallel jobs.
In R2007b, the pre-existing form of parfor was replaced by for i = (drange), but both forms of syntax were recognized in that release. Now parfor has only one context, so parfor statements used in parallel jobs in code for versions prior to R2007a must be modified to use for (drange).
P-Code Scripts. For this release, parfor is not supported in P-code script files.
sim Inside parfor-Loops. Running simulations in a parfor-loop with the sim command at the top level of the loop is not allowed. A sim command visible in a parfor-loop generates an error, although you can call sim inside a function that is called from the loop. Be sure that the various labs running simulations do not have the same working directory, as interference can occur with the simulation data.
When finished performing its distributed evaluation, the dfeval function now destroys the job it created.
If you have any scripts that rely on a job and its data still existing after the completion of dfeval, or that destroy the job after dfeval, these scripts will no longer work.
This version of Parallel Computing Toolbox software is accompanied by a corresponding new version of MATLAB® Distributed Computing Server™ software.
As with every new release, you must upgrade both Parallel Computing Toolbox software and MATLAB Distributed Computing Server software together. These products must be the same version to interact properly with each other.
| Parallel Computing Toolbox™ Release Notes | Version 3.2 (R2007b) Distributed Computing Toolbox™ Software | |
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