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parallel computing in matlab

Hi
I like to use parallel threads, to use the multiple processor cores. (I have a quadcore system and frankly when using matlab despite all mathworks claims I NEVER EVER spotted it using more than 1 core (i.e. 25% CPU usage!) - yes I enabled multithreading support in the preferences).

Anyway. So I want to take things in my own hands. So I started with and example of an "embarresingly parallel task: filtering 12 images. The images are in the variable imgs{...}

tic
I={};
h = fspecial('gaussian',30,30);
for jj= 1:size(imgs,2)
   I{jj}=imfilter(imgs{jj},h);
end
toc

-> this takes about 17seconds and uses 27%CPU.
when I replace the for statement with parfor or
for jj= drange( 1,size(imgs,2))
-> well it takes about 17 seconds and CPU usage is 27%.

great.
WTF am I (or Mathworks) doing wrong?
M.
P.S. using 2007b
0
mschira (4)
6/30/2008 2:57:50 PM
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"Mark Schira" <mschira@unsw.edu.au> schrieb im Newsbeitrag 
news:16559815.1214827100613.JavaMail.jakarta@nitrogen.mathforum.org...
> Hi
> I like to use parallel threads, to use the multiple processor cores. (I 
> have a quadcore system and frankly when using matlab despite all mathworks 
> claims I NEVER EVER spotted it using more than 1 core (i.e. 25% CPU 
> usage!) - yes I enabled multithreading support in the preferences).
>
> Anyway. So I want to take things in my own hands. So I started with and 
> example of an "embarresingly parallel task: filtering 12 images. The 
> images are in the variable imgs{...}
>
> tic
> I={};
> h = fspecial('gaussian',30,30);
> for jj= 1:size(imgs,2)
>   I{jj}=imfilter(imgs{jj},h);
> end
> toc
>
> -> this takes about 17seconds and uses 27%CPU.
> when I replace the for statement with parfor or
> for jj= drange( 1,size(imgs,2))
> -> well it takes about 17 seconds and CPU usage is 27%.
>
> great.
> WTF am I (or Mathworks) doing wrong?
> M.
> P.S. using 2007b

Hi Mark,
- replace the for loop with the drange by a parfor loop
- but more important, use matlab pool to fire up your helpers, so:

matlabpool open local 4
tic
h = fspecial('gaussian', 30, 30);
I = cell(1, size(imgs,2));
parfor (jj=1:size(imgs,2))
  I{jj} = imfilter(imgs{jj}, h);
end
toc


0
6/30/2008 12:38:05 PM
"Mark Schira" <mschira@unsw.edu.au> wrote in message 
news:16559815.1214827100613.JavaMail.jakarta@nitrogen.mathforum.org...
> Hi
> I like to use parallel threads, to use the multiple processor cores. (I 
> have a quadcore system and frankly when using matlab despite all mathworks 
> claims I NEVER EVER spotted it using more than 1 core (i.e. 25% CPU 
> usage!) - yes I enabled multithreading support in the preferences).

Note that only certain functions are multithreaded -- basically some of the 
elementwise functions (trig functions, exponentials, that sort of thing) and 
some of the functions that use the LAPACK and BLAS libraries (like LU, QR, 
matrix multiply, etc.) and only for "sufficiently large" matrices, where the 
speedup gained by using multiple threads is not outweighed by the overhead 
of managing the threads.

One of the common misconceptions I've seen users have about multithreading 
in MATLAB is that everything is multithreaded.  That's not true.

> Anyway. So I want to take things in my own hands. So I started with and 
> example of an "embarresingly parallel task: filtering 12 images. The 
> images are in the variable imgs{...}
>
> tic
> I={};
> h = fspecial('gaussian',30,30);
> for jj= 1:size(imgs,2)
>   I{jj}=imfilter(imgs{jj},h);
> end
> toc
>
> -> this takes about 17seconds and uses 27%CPU.
> when I replace the for statement with parfor or
> for jj= drange( 1,size(imgs,2))
> -> well it takes about 17 seconds and CPU usage is 27%.
>
> great.
> WTF am I (or Mathworks) doing wrong?

Nothing.  The DRANGE call will distribute the body of the code across all 
(numlabs) labs in your MATLAB sessions. Since I don't see any code in your 
example that starts up any workers/labs, that means it's distributing the 
code across all 1 of your labs (the MATLAB instance where you typed this 
code.)  Type "numlabs" just before executing this code and it should return 
1.

http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/for.html

-- 
Steve Lord
slord@mathworks.com 


0
slord (13689)
6/30/2008 1:43:17 PM
Reading along the instructions at

http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/

I came to the conclusion that using a scheduler might be better for what I am looking for. Basically what I want to do at the end of the day is having a main program that creates a user interface and basically interacts with the user displaying questions and waiting for responses.

Every now end then I want to start little "jobs" that will take a while to compute (maybe 20seconds very messy to split in little units) but I want to background them, so that the main look should be able to continue interviewing the user. Until the mail loop will need the results from the little computation there is PLENTY of time. 

I have trouble finding out what is the proper "parallel or distributed computing" framework for this - it all seems a bit complicated - and frankly I haven't even figured out what is the difference between parallel and distributed computing. The latter sounds like it involves multiple computers, but it appears that is not necessarily true. 

It looks to me as in the obove documentation, distributed computing - using a local scheduler is the relevant point for me. There is a simple example of how to schedule multiple rand operations into a "JOB" and start this job, go away do some other stuff and come back and collect the results. Brilliant. That's what I want.

sched = findResource('scheduler','type','local');
job1=createJob(sched);
createTask(job1, @rand, 1, {{3,3} {3,3} {3,3} {3,3} {3,3}});
submit(job1);
results= getAllOutputArguments(job1);

is an easy and straightforward example.

This should make use of the second cpu, right?
M.
0
mschira (4)
7/1/2008 7:11:32 AM
"Mark Schira" <mschira@unsw.edu.au> schrieb im Newsbeitrag 
news:17614083.1214885522460.JavaMail.jakarta@nitrogen.mathforum.org...
> Reading along the instructions at
>
> http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/
>
> I came to the conclusion that using a scheduler might be better for what I 
> am looking for. Basically what I want to do at the end of the day is 
> having a main program that creates a user interface and basically 
> interacts with the user displaying questions and waiting for responses.
>
> Every now end then I want to start little "jobs" that will take a while to 
> compute (maybe 20seconds very messy to split in little units) but I want 
> to background them, so that the main look should be able to continue 
> interviewing the user. Until the mail loop will need the results from the 
> little computation there is PLENTY of time.
>
> I have trouble finding out what is the proper "parallel or distributed 
> computing" framework for this - it all seems a bit complicated - and 
> frankly I haven't even figured out what is the difference between parallel 
> and distributed computing. The latter sounds like it involves multiple 
> computers, but it appears that is not necessarily true.
>
> It looks to me as in the obove documentation, distributed computing - 
> using a local scheduler is the relevant point for me. There is a simple 
> example of how to schedule multiple rand operations into a "JOB" and start 
> this job, go away do some other stuff and come back and collect the 
> results. Brilliant. That's what I want.
>
> sched = findResource('scheduler','type','local');
> job1=createJob(sched);
> createTask(job1, @rand, 1, {{3,3} {3,3} {3,3} {3,3} {3,3}});
> submit(job1);
> results= getAllOutputArguments(job1);
>
> is an easy and straightforward example.
>
> This should make use of the second cpu, right?
> M.

Hi Mark,
yes, that's probably what you are looking for. Some remarks:
- submitting the job to the local scheduler fires up four MATLAB instances 
(take a look at the task manager), so using the job/task mechanism 
automatically does what I suggested to yesterday to you to do manually 
(matlabpool) in order to use the parfor efficiently. matlabpool beforehand 
"reserves" the 4 MATLABs from the local scheduler for you
- the difference between "parallel" and "distributed" in short: to 
distribute a job means that you divide one large task into smaller 
independent (!!) tasks that each worker (MATLAB instance) can process. 
Parallel jobs are those, where those workers (then called labs) need to 
communicate to tackle the problem. Example: Monte Carlo simulation, i.e., 
simulating the same problem with different parameter sets may be 
distributed, but solving a linear system of equations, where the matrix is 
spread over some (or dozens) of machines, is a parallel application
- to distribute or parallelize you don't need several computers. A single 
machine is fine. But if you e.g. have a single core machine you won't see a 
speed up (on the contrary, due to the overhead you will see a slight 
decrease in performance), so testing a parallel/distributed application can 
be done on any machine, but to gain something (speed) you will need multiple 
cores or multiple machines

Hope this helps somewhat,
Titus 


0
7/1/2008 7:30:49 AM
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