There is some confusion here regarding distributed arrays, both in the initial post and in the answer by David Sanchez. Distributed arrays are intended for users who want to work with data distributed across multiple workers, but do not need fine-grained control over exactly how the data is distributed. If fine-grained control over the distribution scheme is required then the spmd construct can be used with codistributed arrays. The spmd block is also useful to examine the data that is actually stored on each worker (see example below).
The initial post was the closest to properly using distributed arrays, but I have tweaked that first bit of code some here:
matlabpool open local 2
tic
x=magic(100);
y=magic(100);
x1 = x(1:5,:)+y(1:5,:);
y1 = x(6:10,:)+y(6:10,:);
dx = distributed(x1);
dy = distributed(y1);
dz = [dx ; dy];
toc
spmd
getLocalPart(dz)
end
If you want to accurately compare timings with serial code, then it would be best to completely remove the distributed array construct from your code and compare to this:
tic
x=magic(100);
y=magic(100);
x1 = x(1:5,:)+y(1:5,:);
y1 = x(6:10,:)+y(6:10,:);
dz = [x1 ; y1];
dz
toc;
If you actually do time these code snippets, you will find that the serial code will be faster because the data you are working with is small. Furthermore the work that is happening in parallel (the concatenation) is not enough to offset the communication cost of distributing the arrays.
Distributed arrays really provide a benefit whenever there is not enough memory in a single instance of MATLAB to store the array you want to work with and the result(s) of the computation.
