Problem with parallel run of Pattern Search (Global Optimization)

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Sina
Sina 2011-4-18
Hi All, I have a problem with running pattern search, using 4 available cpu cores. I change the 'UseParallel' default to 'on' with psoptimset, but it only uses 1 cpu core. matlabpool size returns 4 (so I am connected to 4 cpu cores.) If I use fmincon, with 'UseParallel' --> 'on' it uses 4 cpus, but it does not work with pattern search. Any Idea?

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回答（1 个）

Konrad Malkowski
Konrad Malkowski 2011-4-18
Hi Sina, Have you taken a look at the following:
http://www.mathworks.com/access/helpdesk/help/toolbox/gads/bsc7xh9-2.html
There are certain conditions that have to be met for patternsearch to take advantage of Parallel Computing Toolbox. It is hard to guess from your brief description.
cheers
Konrad
  2 个评论
Sina
Sina 2011-4-19
Hi Konrad; I tried a simple optimization problem. below you can find the code and the results. Using parallel toolbox makes the process much slower !!!
the code:
function test
clear all
clc
x0 = [10 10];
lb = [0 0];
ub = [20 20];
Options=psoptimset('PollMethod','GSSPositiveBasisNp1','UseParallel','always'...
,'CompleteSearch','on','CompletePoll', 'on', 'Vectorized', 'off','Display','off' ); %,'UseParallel','always','Vectorized','on'
% Solving regularly, without parallel toolbox
t=tic;
for j=1:100
x = patternsearch(@OBJ,x0,[],[],[],[],lb,ub,Options);
end
t=toc(t);
matlabpool size;
fprintf('ElapsedTime=%6.3f PoolSize=%2.1f\n',t,ans);
% Solving with parallel toolbox, with different matlabpool sizes
for n=1:4
command = ['matlabpool open ' num2str(n)];
eval(command)
t=tic;
for j=1:100
x = patternsearch(@OBJ,x0,[],[],[],[],lb,ub,Options);
end
t=toc(t);
matlabpool size;
fprintf('ElapsedTime=%6.3f PoolSize=%2.1f\n',t,ans);
matlabpool close
end
end
function y = OBJ(x)
y = ( 4 - 2.1*x(1)^2 + x(1)^(4/3) )*x(1)^2 ...
+ x(1)*x(2) + ( -4 + 4*x(2)^2 )*x(2)^2;
end
the printed results on Command Window:
ElapsedTime= 4.360 PoolSize=0.0
Starting matlabpool using the 'local' configuration ... connected to 1 labs.
ElapsedTime=51.672 PoolSize=1.0
Sending a stop signal to all the labs ... stopped.
Starting matlabpool using the 'local' configuration ... connected to 2 labs.
ElapsedTime=57.076 PoolSize=2.0
Sending a stop signal to all the labs ... stopped.
Starting matlabpool using the 'local' configuration ... connected to 3 labs.
ElapsedTime=54.798 PoolSize=3.0
Sending a stop signal to all the labs ... stopped.
Starting matlabpool using the 'local' configuration ... connected to 4 labs.
ElapsedTime=57.315 PoolSize=4.0
Sending a stop signal to all the labs ... stopped.
>>
Konrad Malkowski
Konrad Malkowski 2011-4-20
Hi Sina,
This looks like a case where the overhead of parallel computation (i.e., communication, synchronization, etc) outweigh the performance benefits of running in parallel. Try a problem that requires a much longer execution time per iteration of the objective function.

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