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| Contents | Index |
| Learn more about Parallel Computing Toolbox |
This table summarizes what is new in Version 3.1 (R2007a):
| 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
A local scheduler allows you to schedule jobs and run up to four workers or labs on a single MATLAB client machine without requiring engine licenses. These workers/labs can run distributed jobs or parallel jobs, including pmode sessions, for all products for which the MATLAB client is licensed. This local scheduler and its workers do not require a job manager or third-party scheduler.
The interactive parallel mode (pmode) has a new interface. The pmode command input and displays of the lab outputs are provided in a user interface that you can separate from the MATLAB client Command Window.
In previous versions of Distributed Computing Toolbox, the pmode interface used the MATLAB Command Window, with the pmode input using a different prompt. The output from the labs was intermingled with the MATLAB client output.
If you start pmode without specifying a configuration,
pmode start
pmode automatically starts a parallel job using the local scheduler with labs running on the client machine. For more information about running pmode, see Interactive Parallel Computation with pmode in the Distributed Computing Toolbox documentation.
In the previous version of the toolbox, when pmode was started without specifying a configuration, it searched the network for the first available job manager to use as a scheduler.
The createTask function can now create a vector of tasks in a single call when you provide a cell array of cell arrays for input arguments. For full details, see the createTask reference page.
In previous versions of the distributed computing products, if your task function had an input argument that was a cell array of cell arrays, your code will need to be modified to run the same way in this release.
For example, your old code may have been written as follows so that the function myfun gets four cell array input arguments:
createTask(j, @myfun, 1, {{C1} {C2} {C3} {C4}})In this new version, the same code will produce four tasks. To get the old functionality, you must wrap the four cell arrays in another cell array, so that createTask knows to create only one task.
createTask(j, @myfun, 1, { {{C1} {C2} {C3} {C4}} })There are several submit and decode functions provided with the toolbox for your use with the generic scheduler interface. These files are in the directory
matlabroot/toolbox/distcomp/examples/integration
This version of the toolbox includes new subdirectories for Platform LSF and PBS, to support network configurations in which the client and worker computers do not share a file system. For more information, see Supplied Submit and Decode Functions in the Distributed Computing Toolbox documentation.
The Jobs property of a job manager object now contains the jobs in the order in which they were created, as indicated by the ID property of each job. Similarly, the findJob function returns jobs sequenced by their ID, unless otherwise specified. This change makes job manager behavior consistent with the behavior of third-party schedulers.
In previous versions of the distributed computing products, when using a job manager, jobs were arranged in the Jobs property or by findJob according to the status of the job.
When you create distributed computing objects (scheduler, job, or task) without a semicolon at the end of the command, the object information is displayed in a new format. This new format is also shown when you use the display function to view an object or simply type the object name at the command line.
With this enhancement, the output format shown when creating an object has changed.
Several MATLAB functions have been enhanced to work on distributed arrays:
For a complete list of MATLAB functions that are enhanced to work on distributed arrays, see Using MATLAB Functions on Codistributed Arrays in the Distributed Computing Toolbox documentation.
The function darray now defines how an array is distributed among the labs in a parallel job.
In the previous version of the toolbox, the distributor function was used to define how an array was distributed. In many cases, you can replace a call to distributor with a call to darray. For example, if you used distributor without arguments as an input to an array constructor,
rand(m, n, distributor());
you can update the code to read,
rand(m, n, darray());
The random generator seed is now initialized based on the task ID for distributed jobs, or the labindex for parallel jobs (including pmode). This ensures that the set of random numbers generated for each task or lab within a job is unique, even when you have more than 82 tasks or labs.
In the previous version of the distributed computing products, the rand function could by default generate the same set of numbers for some tasks or labs when these exceeded 82 for a job.
Despite the ability in MATLAB software to perform multithreaded computations on multiple-CPU machines, the workers and labs running distributed and parallel jobs perform only single-threaded computations, so that multiprocessor cluster machines can better accommodate multiple workers or labs.
 | Version 3.2 (R2007b) Distributed Computing Toolbox Software | Version 3.0 (R2006b) Distributed Computing Toolbox Software |  |
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