Asked by huda nawaf
on 13 May 2013

hi, I used this simple code using parallel processing to see the effect of using two cores, then I can use parallel processing with complex code. But I fount out the time when use the same code without using parallel processing is less. I thought when use two cores , the two cores will process a part of dz matrix at the same time, but it seem they not process at the same time . why?

matlabpool open local 2 tic x=magic(100); y=magic(100); x1 = x(1:5,:)+y(1:5,:); y1 = x(6:10,:)+y(6:10,:); dx = distributed(x1); dy = distributed(y1); dz = [dx ; dy]; spmd dz end toc

this code without using parallel processing

tic x=magic(100); y=magic(100); x1 = x(1:5,:)+y(1:5,:); y1 = x(6:10,:)+y(6:10,:); dx = distributed(x1); dy = distributed(y1); dz = [dx ; dy]; dz toc;

Thanks in advance

Answer by Thomas
on 13 May 2013

Huda,

You are running a small jobs which might have more overhead parallelizing sincce the number of iterations or jobs size is small. In order to achieve a performance boost, a large number of iterations must be performed or the size of dataset large.

http://vtchl.illinois.edu/sites/hydrolab.dev.engr.illinois.edu/files/MATLAB_Report.pdf This shows some performance considerations for MATLAB http://vtchl.illinois.edu/node/537

huda nawaf
on 14 May 2013

Thanks,

I tried magic(10000), but the running time is still longer in case of parallel processing.

please, help me to write code by which I get running time with parrallel is less than serial.

I have complex code , I want to use parallel processing with my code. please please, it is not necessary use spmd, any type of parallel processingb because my complex code has long runnig time and I wont to use parallel processing.

I spent much time to read parallel processing , but I could not write code successfully

Answer by David Sanchez
on 14 May 2013

Try your code in this way:

matlabpool open local 2 tic spmd x=magic(100); y=magic(100); x1 = x(1:5,:)+y(1:5,:); y1 = x(6:10,:)+y(6:10,:); dx = distributed(x1); dy = distributed(y1); dz = [dx ; dy]; dz end toc

huda nawaf
on 14 May 2013

thanks,

I used

matlabpool open local 2 spmd tic x=magic(1000); y=magic(1000); x1 = x(1:5,:)+y(1:5,:); y1 = x(6:10,:)+y(6:10,:); dx = distributed(x1); dy = distributed(y1); dz = [dx; dy] toc end

The running time is 1.03

while the running time of serial code is 0.28

tic x=magic(1000); y=magic(1000); x1 = x(1:5,:)+y(1:5,:); y1 = x(6:10,:)+y(6:10,:); dz = [x1; y1]; toc

Answer by Jill Reese
on 15 May 2013

Edited by Jill Reese
on 15 May 2013

There is some confusion here regarding distributed arrays, both in the initial post and in the answer by David Sanchez. Distributed arrays are intended for users who want to work with data distributed across multiple workers, but do not need fine-grained control over exactly how the data is distributed. If fine-grained control over the distribution scheme **is** required then the spmd construct can be used with codistributed arrays. The spmd block is also useful to examine the data that is actually stored on each worker (see example below).

The initial post was the closest to properly using distributed arrays, but I have tweaked that first bit of code some here:

matlabpool open local 2 tic x=magic(100); y=magic(100); x1 = x(1:5,:)+y(1:5,:); % indexing is happening in the client MATLAB y1 = x(6:10,:)+y(6:10,:);

dx = distributed(x1); % distribute these matrices across two workers dy = distributed(y1);

dz = [dx ; dy]; % this concatenation is happening in parallel on the workers toc

% Use the spmd construct to see what data is actually stored on each worker spmd getLocalPart(dz) end

If you want to accurately compare timings with serial code, then it would be best to completely remove the distributed array construct from your code and compare to this:

tic x=magic(100); y=magic(100); x1 = x(1:5,:)+y(1:5,:); y1 = x(6:10,:)+y(6:10,:); dz = [x1 ; y1]; dz toc;

If you actually do time these code snippets, you will find that the serial code will be faster because the data you are working with is small. Furthermore the work that is happening in parallel (the concatenation) is not enough to offset the communication cost of distributing the arrays.

Distributed arrays really provide a benefit whenever there is not enough memory in a single instance of MATLAB to store the array you want to work with and the result(s) of the computation.

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Jill Reese
on 17 May 2013

Have you written your own code or are you leveraging the Bioinformatics Toolbox? The Bioinformatics Toolbox has built-in parallel support for the multialign and seqpdist functions.

huda nawaf
on 17 May 2013

I wrote my own code because what exist do not meet my goal, I used sequence alignment in social science field not in bioinformatic.

huda nawaf
on 19 May 2013

Please Jill, can u help me to write my code in proper way, this will much help me.

thanks in advance

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