MATLAB: Split a matrix on parallel computing with spmd

MATLABparallel computingParallel Computing Toolbox

As I can split a vector 4×18 in four workers each with a 1×18 table, selecting the vector for each worker in parallel computing with spmd.

Best Answer

You could do that using distributed arrays. For example

    spmd
        d = codistributed(rand(4, 18), codistributor1d(1))
    end

will result in each worker storing a 1x18 portion of d.

Related Solutions

MATLAB: MATLAB Distributed Computing Server

Yes, you can open a matlabpool using MDCS workers and run PARFOR and SPMD.

MATLAB: When to use codistributed arrays

Distributed arrays are most useful when you do not have enough memory to store an entire array on a single machine. By distributing chunks of the original array across all the workers in the pool, you can perform operations on the entire array that you previously couldn't even store.

I would suggest that you take a step back and start by working with distributed arrays rather than codistributed arrays. This allows MATLAB to choose a default distribution scheme for you.

A = rand(6400,6400);
matlabpool open 
dA = distributed(A); % let MATLAB pick a default distribution scheme for the 
                     % distributed array, dA
R = chol(dA);        % example of a function that works for 
                     % distributed arrays - no spmd required

With distributed arrays, you can get started without having to worry about what distribution scheme to use. If you are interested, you can always query the distribution scheme that is currently used by a distributed array like so:

% dA is the distributed array created as above
spmd
codistr = getCodistributor(dA) % inside spmd we can "view" dA as a 
                               % codistributed array and access its
                               % distribution scheme
end

Once your code is working correctly with distributed arrays, you can make minor changes to use codistributed arrays with a specific distribution scheme. Changing the default distribution scheme may improve performance, but as Matt J mentions, choosing the most efficient distribution scheme is problem dependent and will depend heavily on the operations that you want to call.

Best Answer

Related Solutions

MATLAB: MATLAB Distributed Computing Server

MATLAB: When to use codistributed arrays

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