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| R2012a Documentation → Parallel Computing Toolbox | |
Learn more about Parallel Computing Toolbox |
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| Contents | Index |
This table summarizes what is new in Version 5.1 (R2011a):
| New Features and Changes | Version Compatibility Considerations | Fixed Bugs and Known Problems |
|---|---|---|
Yes | Yes — Details labeled as Compatibility Considerations, below. See also Summary. | Bug
Reports |
MATLAB Compiler generated standalone executables and libraries from parallel applications can now launch up to eight local workers without requiring MATLAB Distributed Computing Server software.
When you first open a MATLAB pool from your desktop session, an indicator appears in the lower-right corner of the desktop to show that this desktop session is connected to an open pool. The number indicates how many workers are in the pool.
When you close the pool, the indicator remains displayed and shows a value of 0.
The following new static methods directly create GPUArray objects:
parallel.gpu.GPUArray.linspace parallel.gpu.GPUArray.logspace
The following functions are enhanced to support GPUArray data:
cat colon conv conv2 cumsum cumprod eps filter filter2 horzcat meshgrid ndgrid plot subsasgn subsindex subsref vertcat
and all the plotting related functions.
Because GPUArray now supports subsasgn and subsref, you can index into a GPUArray for assigning and reading individual elements.
For example, create a GPUArray and assign the value of an element:
n = 1000; D = parallel.gpu.GPUArray.eye(n); D(1,n) = pi
Create a GPUArray and read the value of an element back into the MATLAB workspace:
m = 500; D = parallel.gpu.GPUArray.eye(m); one = gather(D(m,m))
GPU support is extended to include the following MATLAB code in functions called by arrayfun to run on the GPU:
&, |, ~, &&, ||, while, if, else, elseif, for, return, break, continue, eps
You can now call eps with string inputs, so your MATLAB code running on the GPU can include eps('single') and eps('double').
This version of Parallel Computing Toolbox GPU functionality supports only NVIDIA CUDA device driver 3.2.
Compatibility Considerations. Earlier versions of the toolbox supported earlier versions of CUDA device driver. If you have an older driver, you must upgrade to CUDA device driver version 3.2.
The following functions are enhanced to support distributed arrays, supporting all forms of codistributor (1-D and 2DBC):
arrayfun cat reshape
The mtimes function now supports distributed arrays that use a 2-D block-cyclic (2DBC) distribution scheme, and distributed arrays that use 1-D distribution with a distribution dimension greater than 2. Previously, mtimes supported only 1-D distribution with a distribution dimension of 1 or 2.
The mtimes function now returns a distributed array when only one of its inputs is distributed, similar to its behavior for two distributed inputs.
Compatibility Considerations. In previous releases, mtimes returned a replicated array when one input was distributed and the other input was replicated. Now it returns a distributed array.
You can now create nested for-loops inside a parfor-loop, and you can use both the parfor-loop and for-loop variables directly as indices for the sliced array inside the nested loop. See Nested Loops.
The parallel computing products now support Microsoft® Windows® HPC Server on 32-bit Windows clients.
The findResource function can search Active Directory to identify your cluster head node. For more information, see the findResource reference page.
You can now start and stop mdce services on remote hosts from Admin Center. For more information, see Start mdce Service.
New functionality is available that lets you mirror job data from a remote cluster to a local data location. This supports the generic scheduler interface when making remote submissions to a scheduler or when using a nonshared file system. For more information, see the RemoteClusterAccess object reference page.
 | Version 5.2 (R2011b) Parallel Computing Toolbox Software | Version 5.0 (R2010b) Parallel Computing Toolbox Software |  |
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