"Doug Hull" <hull@mathworks.SPAMPROOFcom> wrote in message <gl4nrp$omn$1@fred.mathworks.com>...
>
> > Dear Doug,
> > thanks very much for your help and correct my msprint,
> > now this code works more efficiently.
> > But, still I would like to reduce the time of my code,
> > do you think using (parfor) instead of (for) in one loop with the
> > computer parallel toolbox can reduce time in this code?
> > Thanks in advance,
> > Jose.
>
> Jose,
>
> I would caution against premature optimization. Get your algorithm working and then see if it needs optimization. People very often can work at optimizing code without knowing if it really needs it.
>
> That being said, your code is a candidate for "embarrassingly parallel". Essentially, you are doing a set number of iterations, 2,000,000 (nv * nx). Each of these iterations are completely independent of one another. Thus, you could do them in any order, or all at the same time (if you had 2,000,000 MATLAB sessions)
>
> Basically, the more processors you throw at this, the faster it will go.
>
> Here is a job time estimator that I made a few years ago. It should still be a reasonable guess at how long a job will take on a cluster.
>
> http://www.mathworks.com/matlabcentral/fileexchange/13075
>
> Doug
Hello Doug, thanks for your reply.
Now, I installed parallel computing toolbox to check it, but something is wrong
in my approach to get better time parallelizing it:
%code without parallelization
clc
clear all
d = 1;
nv=1000;
nx=2000;
x=rand(nx,2); % data 2D
sigma=cov(x);
bbb = inv(sigma); %%%%% MOVED
y=zeros(1,nx);
tic
for j=1:nv % Loop in free parameter mu
mu= [rand(1,1) rand(1,1)];
for i=1:nx % Loop to calculate the likelihood function (lf)
ir=round(1+(nx1)*rand(1,1));
aaa = (x(ir,:)mu);
ccc = (x(ir,:)mu)';
y(i)=aaa*bbb*ccc;
end
lf=0.5*sum(y)+nx*0.5*log((det(sigma)))+nx*0.5*d*log(2.0*pi); % likelihood
p1(j)=lf;
end
p=exp(sum(p1(j))/nv)
toc
22.60 sec.
%code using parallelization
Now, i start matlabpool and suply (parfor) by (for) in the second loop of nx:
Starting matlabpool using the parallel configuration 'local'.
Waiting for parallel job to start...
Connected to a matlabpool session with 4 labs.
clc
clear all
d = 1;
nv=1000;
nx=2000;
x=rand(nx,2); % data 2D
sigma=cov(x);
bbb = inv(sigma); %%%%% MOVED
y=zeros(1,nx);
tic
for j=1:nv % Loop in free parameter mu
mu= [rand(1,1) rand(1,1)];
parfor (i=1:nx) % ONLY MODIFIED IT IN MY CODE.
ir=round(1+(nx1)*rand(1,1));
aaa = (x(ir,:)mu);
ccc = (x(ir,:)mu)';
y(i)=aaa*bbb*ccc;
end
lf=0.5*sum(y)+nx*0.5*log((det(sigma)))+nx*0.5*d*log(2.0*pi); % likelihood
p1(j)=lf;
end
p=exp(sum(p1(j))/nv)
toc
101.39 sec
The time is more slower paralelizing it,
Please Doug, do you know what I am doing wrong?
My laptop is a centrino duo ...i.e with two processors.
Thanks in advance.
Jose.
