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| Contents | Index |
| Learn more about Parallel Computing Toolbox |
This table summarizes what is new in Version 4.2 (R2009b):
| 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 | Printable Release Notes: PDF |
New features and changes introduced in this version are
A new form of distributed arrays provides direct access from the client to data stored on the workers in a MATLAB pool. Distributed arrays have the same appearance and rules of indexing as regular arrays.
You can distribute an existing array from the client workspace with the command
D = distributed(X)
where X is an array in the client, and D is a distributed array with its data on the workers in the MATLAB pool. Distributing an array is performed outside an spmd statement, but a MATLAB pool must be open.
Codistributed arrays that you create on the workers within spmd statements are accessible on the client as distributed arrays.
The following new functions and methods support distributed arrays.
| Function Name | Description |
|---|---|
| distributed | Distribute existing array from client workspace to workers |
| distributed.rand, distributed.ones, etc. | Create distributed array consistent with indicated method, constructing on workers only |
| gather | Transfer data from MATLAB pool workers to client |
| isdistributed | True for distributed array |
| C(x,y) | Indexing into distributed array C on client to access data stored as codistributed arrays on workers |
As part of the general enhancements for distributed arrays, several changes to the codistributed interface appear in this release.
The following table summarizes the changes in function names relating to codistributed arrays.
| Old Function Name | New Function Name |
|---|---|
| codcolon | codistributed.colon |
| codistributed(..., 'convert') | codistributed(...) |
| codistributed(...) without 'convert' option | codistributed.build |
| codistributed(L, D) using distribution scheme of D to define that of L | codistributed.build(L, getCodistributor(D)) |
| codistributor('1d', ...) | Still available, but can also use codistributor1d |
| codistributor('2d', ...) | codistributor('2dbc', ...) or codistributor2dbc |
| codistributor(arrayname) | getCodistributor |
| defaultLabGrid | codistributor2dbc.defaultLabGrid |
| defaultPartition | codistributor1d.defaultPartition |
| isa(X, 'codistributed') | Still available, but can also use iscodistributed(X) |
| localPart | getLocalPart |
| redistribute(D) using default distribution scheme | redistribute(D, codistributor()) |
| redistribute(D1, D2) using distribution scheme of D2 to define that of D1 | redistribute(D1, getCodistributor(D2)) |
Some object methods are now properties:
| Old Method Name | New Property Name |
|---|---|
| blockSize(codistObj) | codistObj.BlockSize |
| defaultBlockSize | codistributor2dbc.defaultBlockSize |
| distributionDimension(codistObj) | codistObj.Dimension |
| distributionPartition(codistObj) | codistObj.Partition |
| labGrid(codistObj) | codistObj.LabGrid |
Admin Center has several small enhancements, including more conveniently located menu choices, modified dialog boxes, properties dialog boxes for listed items, etc.
Enhancements to the matlabpool command let you add or update file dependencies in a running MATLAB pool. The new forms of the command are
matlabpool('addfiledependencies', filedepCell)
matlabpool updatefiledependencieswhere filedepCell is a cell array of strings, identical in form to those you use when adding file dependencies to a job or when you open a MATLAB pool. The updatefiledependencies option replicates any file dependency changes to all the labs in the pool.
The globalIndices function now requires that you specify the dimension of distribution as its second argument. Because this argument is required, it must precede the optional argument specifying the lab.
In previous toolbox versions, the globalIndices function accepted the lab argument before the dimension argument, and both were optional. Now the dimension argument is required, and it must precede the optional lab argument.
Using job templates and job description files with Windows HPC Server 2008 lets you specify nodes and other scheduler properties for evaluating your jobs. To support these features, the ccsscheduler object has new properties:
ClusterVersion — A string set to 'CCS' or 'HPCServer2008'
JobTemplate — A string set to the name of the job template to use for all jobs
JobDescriptionFile — A string set to the name of the XML file defining a base state for job creation
CCS is now just one of multiple versions of HPC Server. While 'ccs' is still acceptable as a type of scheduler for the findResource function, you can also use 'hpcserver' for this purpose. In the Configurations Manager, the new scheduler type is available by selecting File > New > hpcserver (ccs).
Several new MATLAB files are available to demonstrate HPC Challenge benchmark performance. You can find the files in the folder matlabroot/toolbox/distcomp/examples/benchmark/hpcchallenge. Each file is self-documented with explanatory comments.
The pctconfig function now lets you specify a range of ports for the Parallel Computing Toolbox™ client session to use. This range also includes ports used for a pmode session.
You now specify the range of pctconfig ports with the 'portrange' property; you no longer use the 'port' property. As any client pmode session uses those ports of the 'portrange' setting, you no longer use the 'pmodeport' property.
The random number generator of the MATLAB workers now use a slightly different seed from previous releases, so that all the MATLAB workers and the client have separate random number streams.
In past releases, while all the workers running a job had separate random number streams, the client had the same stream as one of the workers. Now the workers all have unique random number streams different from that of the client.
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 and MATLAB Distributed Computing Server products 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.
 | Parallel Computing Toolbox Release Notes | Version 4.1 (R2009a) Parallel Computing Toolbox Software |  |
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