User`s guide

R2015a
1-2
Support for mapreduce function on any cluster that supports parallel
pools
You can now run parallel mapreduce on any cluster that supports a parallel pool. For
more information, see “Run mapreduce on a Parallel Pool”.
Sparse arrays with GPU-enabled functions
This release supports sparse arrays on a GPU. You can create a sparse gpuArray either
by calling sparse with a gpuArray input, or by calling gpuArray with a sparse input.
The following functions support sparse gpuArrays.
classUnderlying
conj
ctranspose
end
find
full
gpuArray.speye
imag
isaUnderlying
isempty
isfloat
isinteger
islogical
isnumeric
isreal
issparse
length
mtimes
ndims
nonzeros
nnz
numel
nzmax
real
size
sparse
spones
transpose
Note the following for some of these functions:
• gpuArray.speye is a static constructor method.
• sparse supports only single-argument syntax.
• mtimes does not support the case of full-matrix times a sparse-matrix.
For more information on this topic, see “Sparse Arrays on a GPU”.
A new C function, mxGPUIsSparse, is available for the MEX interface, to query whether
a gpuArray is sparse or not. However, even though the MEX interface can query
properties of a sparse gpuArray, its functions cannot access sparse gpuArray elements.
Additional GPU-enabled MATLAB functions
The following functions are new in their support for gpuArrays: