CODEWITHSUNDEEP
matlabparallel-processingspmd
omid kiasat
omid kiasat

Reputation: 31

How to use SPMD for different input variables and save the output in order?

I am using a simulated annealing algorithm to optimize my problem, I have to do it for 100 different input variables and save the output for all variables in order. the problem is that I don't know how to implement spmd in my code to do parallel computing so that each input run on one CPU core and the final results stored in a matrix with 100 rows. I've tried to put it before the first for loop but it only returns a composite consists of 4 elements, since my CPU has 4 cores. Here is my code

spmd
for v=1:100
posmat=loading_param(Matrix,v);
nvar=size(posmat,2); 
popsize=50;
maxiter=20;
T0=1000;
Tf=1;
Tdamp=((T0-Tf)/maxiter);
nn=5;
T=T0;

%% initial population
tic
emp.var=[];
emp.fit=inf;
pop=repmat(emp,popsize,1);
    for i=1:popsize
     pop(i).var=randperm(nvar);
     pop_double=pop(i).var;
     posmat_new=tabdil(nvar,pop_double,posmat);
     dis=cij(posmat_new);
     pop(i).fit=fittness(dis);
    end
   [value,index]=min([pop.fit]);
   gpop=pop(index);

%% algorithm main loop 
BEST=zeros(maxiter,1);

for iter=1:maxiter
    for i=1:popsize
        bnpop=emp;
        for j=1:nn

            npop=create_new_pop(pop(j),nvar,posmat);

            if npop.fit<bnpop.fit
                bnpop=npop;

            end

        end

        if bnpop.fit<pop(i).fit
            pop(i)=bnpop;
        else
            E=bnpop.fit-pop(i).fit;
            pr=exp(-E/T);
            if rand<pr
                pop(i)=bnpop;
            end
        end
    end

    T=T-Tdamp;


 [value,index]=min([pop.fit]);
 if value<gpop.fit
 gpop=pop(index);

 BEST(iter)=gpop.fit;
 disp([ 'iter= ' num2str(iter) 'BEST=' num2str(BEST(iter))])
 end
end



%% algorithm results

disp([ ' Best solution=' num2str(gpop.var)])
disp([ ' Best fittness=' num2str(gpop.fit)])
disp([ ' Best time=' num2str(toc)])

bnpop_all(d,:)=bnpop.var;
d=d+1;

end %end of main for loop
end % end of spmd

Upvotes: 0

Views: 467

Answers (1)

Adriaan
Adriaan

Reputation: 18177

From the documentation on spmd:

Values returning from the body of an spmd statement are converted to Composite objects on the MATLAB client. A Composite object contains references to the values stored on the remote MATLAB workers, and those values can be retrieved using cell-array indexing. The actual data on the workers remains available on the workers for subsequent spmd execution, so long as the Composite exists on the client and the parallel pool remains open.

Thus the output is a composite with 4 elements, since you have 4 CPU cores, so output{1} gives you the first element, output{2} the second etc. Just concatenate those to get your output in a single matrix.

Your code at this point just runs four times, one complete 100 iteration for loop per worker. An easier way to solve this, is to use parfor instead of spmd, as you can leave your loop the same. If you want to use spmd, first cut your v into four pieces (of 25 elements each), then on each worker iterate over just those 25 elements.

Seeing your code, with its three nested loops, I suggest not parallellising now, but instead try to profile your code, find out where the bottlenecks are, and try to speed up those. Probably trying to vectorise your nested loops will improve a lot already.

Upvotes: 2

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