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

Question

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

Answer 1

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.