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Using parfor in Matlab


We all know that loops don’t behave well in Matlab. Whenever it is possible to vectorize the code (i.e. use vectors and matrices to do simultaneous operations, instead of one at a time) significant speed-up is possible. However, there are complex tasks which cannot be vectorized and loops cannot be avoided. I recently needed to compute eigenvalues for some 10 million domains. Since the computations are independent, they could be run in parallel. Fortunately Matlab offers a simple way to do this, using parfor.

There are some basic rules one need to respect to use parfor efficiently:

  1. Don’t use parfor if vectorization is possible. If the task is not vectorizable and computations are independent, then parfor is the way to go.
  2. Variables used in each computation should not overlap between processors. This is for obvious reasons: if two processors try to change the same variable using different values, the computations will be meaningless in the end.
  3. You can use an array or cell to store the results given by each processor, with the restriction that processors should work on disjoint parts of the array, so there is no overlap.

The most restrictive requirement is the fact that one cannot use the same variables in the computations for different processors. In order to do this, the simplest way I found was to use a function for the body of the loop. When using a matlab function, all variables are local, so when running the same function in parallel, the variables won’t overlap, since they are local to each function.

So instead of doing something like

parfor i = 1:N
   commands ...
   array(i) = result
end

you can do the following:

parfor i=1:N
   array(i) = func(i);
end

function res = func(i)
   commands...

This should work very well and no conflict between variables will appear. Make sure to initialize the array before running the parfor, a classical Matlab speedup trick: array = zeros(1,N). Of course, you could have multiple outputs and the output array could be a matrix.

There is another trick to remember if the parpool cannot initialize. It seems that the parallel cluster doesn’t like all the things present in the path sometimes. Before running parfor try the commands

c = parcluster('local');
c.parpool

If you recieve an error, then run

restoredefaultpath
c = parcluster('local');
c.parpool

and add to path just the right folders for your code to work.

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