Improving parfor Performance

Where to Create Arrays

With a parfor-loop, it might be faster to have each MATLAB^® worker create its own arrays or portions of them in parallel, rather than to create a large array in the client before the loop and send it out to all the workers separately. Having each worker create its own copy of these arrays inside the loop saves the time of transferring the data from client to workers, because all the workers can be creating it at the same time. This might challenge your usual practice to do as much variable initialization before a for-loop as possible, so that you do not needlessly repeat it inside the loop.

Whether to create arrays before the parfor-loop or inside the parfor-loop depends on the size of the arrays, the time needed to create them, whether the workers need all or part of the arrays, the number of loop iterations that each worker performs, and other factors. While many for-loops can be directly converted to parfor-loops, even in these cases there might be other issues involved in optimizing your code.

Another option to consider is to use the parallel.pool.Constant function to establish variables on the pool workers before the loop. These variables remain on the workers after the loop finishes, and remain available for multiple parfor-loops.

Slicing Arrays

If a variable is initialized before a parfor-loop, then used inside the parfor-loop, it has to be passed to each MATLAB worker evaluating the loop iterations. Only those variables used inside the loop are passed from the client workspace. However, if all occurrences of the variable are indexed by the loop variable, each worker receives only the part of the array it needs.

Optimizing on Local vs. Cluster Workers

Running your code on local workers might offer the convenience of testing your application without requiring the use of cluster resources. However, there are certain drawbacks or limitations with using local workers. Because the transfer of data does not occur over the network, transfer behavior on local workers might not be indicative of how it will typically occur over a network.

With local workers, because all the MATLAB worker sessions are running on the same machine, you might not see any performance improvement from a parfor-loop regarding execution time. This can depend on many factors, including how many processors and cores your machine has. You might experiment to see if it is faster to create the arrays 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.

Documentation