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Asked by Sascha on 18 Jun 2013

Hi all,

for a student project, I work with the "Parallel Computing Toolbox". To discover it's functionality I make simple matrix operations with distributed matrizes.

With an open matlapool (12 workers) I use (for example) the following code, on a computer with 16 cores:

M1 = rand (i,i); M2 = rand (i,i);

tic M = M1 * M2; toc

M1 = distributed(M1); M2 = distributed(M2);

tic M = M1 * M2; toc

While execution of this code I run "ksysguard" to observe the cpu-usage. During the first multiplication (with "normal" matrizes), the cpu-usage of all 16 cores is at 100%. While the second multiplication runs, the usage of 12 cores is at 100% (I think this could be correct, cause of the 12 workers). I even notice that the first multiplication is much quicker (I tested it for values of n up to 20.000), and gets quicker with bigger matrizes.

Even when I close the matlab pool and do a matrix multiplication, all 16 cores have a usage of 100%. Does this mean that matlab does parallel computation at default?

I hope you understand, what I am wondering about. Many thanks in advance for your suggestions.

Answer by Shashank Prasanna on 18 Jun 2013

Accepted answer

Most math operations are inherently multithreaded. There is a list you will find on our external page:

http://www.mathworks.com/support/solutions/en/data/1-4PG4AN/

Jill will correct me if I am wrong, but each worker in the matlab pool is always single threaded. Which means the multithreaded operations will run single threaded on a worker but you have the advantage to scale to a large number of machines.

You may also find this link helpful which explains the difference.

Answer by Jill Reese on 18 Jun 2013

A number of MATLAB functions are multithreaded, and matrix multiplication (*) is one of them. This is what you are seeing during the first multiplication (with "normal" matrices).

Answer by Sascha on 19 Jun 2013

Thank you both for your answers. You helped me very much!

Shashank Prasanna on 19 Jun 2013

Sascha, make sure you go ahead and accept an answer. This makes others who have questions similar to yours get their answer quickly by looking at the accepted answer.

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