Parallel programming: defining tasks

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Daniel Jaló
Daniel Jaló on 10 Feb 2013
I made a simple function, compute(m,n), that calculates the number of primes between m and n. I used this function to test the performance improvement when using parfor and spmd against a simple for cycle. I used the following cycle to test it:
A = [3 480000 830000 1100000];
NumPrimes = 0;
t = tic;
for n = 1:3
NumPrimes = NumPrimes + compute(A(n),A(n+1));
As expected using parfor, or rewriting the cycle with spmd, gave me better results than with the serial for cycle.
Now comes the problem. I tried to run a job, defining each task such that each worker would take care of a different subset of numbers. Lab 1 would compute the number of primes from 3 to 480000, Lab 2 from 480000 to 830000, etc... To do it I wrote the following code:
job = createJob('configuration','local','FileDependencies',{'compute.m'});
A = [3 480000 830000 1100000];
NumPrimes = 0;
t = tic;
for n=1:3
out = getAllOutputArguments(job);
NumPrimes = sum([out{:}]);
When I run this code there are 2 possible outcomes: If I have 3 labs (matlabpool open 3) the code will run but it will take about 3x the time it takes with a serial code. If I have 4 labs it will run forever, until I eventually cancel it.
My doubts are: - Why isn't my code speeding up in relation to the serial version? - When I use the function createTask can't I decide which Lab takes care of the task or is it assigned by order? - Can I have more tasks than workers? If so how will they take care of the tasks? Each lab will take care of one of the first 4 tasks, and when they complete them move on to the next uncomplete task?
If anyone could clear my mind I'd really appreciate. Thanks!

Answers (1)

Thomas Ibbotson
Thomas Ibbotson on 11 Feb 2013
Hi Daniel,
Thanks, Tom

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