MATLAB PARALLEL COMPUTING TOOLBOX - S Guia do Utilizador descarregar pdf (Página 71)

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101

Improving parfor Performance

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before the loop (as shown on the left below), rather than have each worker create its own

arrays inside the loop (as shown on the right).

Try the following examples running a parallel pool locally, and notice the difference in

time execution for each loop. First open a local parallel pool:

parpool('local')

Then enter the following examples. (If you are viewing this documentation in the

MATLAB help browser, highlight each segment of code below, right-click, and select

Evaluate Selection in the context menu to execute the block in MATLAB. That way the

time measurement will not include the time required to paste or type.)

tic;

n = 200;

M = magic(n);

R = rand(n);

parfor i = 1:n

A(i) = sum(M(i,:).*R(n+1-i,:));

end

toc

tic;

n = 200;

parfor i = 1:n

M = magic(n);

R = rand(n);

A(i) = sum(M(i,:).*R(n+1-i,:));

end

toc

Running on a remote cluster, you might find different behavior, as workers can

simultaneously create their arrays, saving transfer time. Therefore, code that is

optimized for local workers might not be optimized for cluster workers, and vice versa.

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