There are multiple ways to improve this; I'm not certain if you're already doing this, so two improvements without parallelisation:
- preallocate space for EOR
- vectorise the expressions where possible, eg:
A=repmat(reshape(a,[],1,1,1),[1 length(b) length(c) length(d)]);
B=repmat(reshape(b,1,[],1,1),[length(a) 1 length(c) length(d)]);
C=repmat(reshape(c,1,1,[],1),[length(a) length(b) 1 length(d)]);
D=repmat(reshape(d,1,1,1,[]),[length(a) length(b) length(c) 1]);
E2 = A.^2.*B + C.*D.^2 + A.*C;
Within the parallel toolbox, turning the inner loop into a forloop should run, but is likely to be slower due to memory overhead. The one you want to parellise is probably the outermost loop, but as set it won't be run because MATLAB doesn't know what values j,ii,jj hold when it creates the forloop. An alternative would be:
E=struct('x',[]);
parfor i=1:length(a)
E(i).x = zeros(length(b),length(c),length(d));
for j=1:length(b)
for ii=1:length(c)
for jj=1:length(d)
E(i).x(j,ii,jj) = a(i)^2 * b(j) + c(ii) * d(jj) ^2 + a(i) * c(ii);
end
end
end
end
You then need to turn the structure E back into the matrix you need. Without testing I would expect this to still be substantially slower than the vectorised version above, although it may be you have some cases which you cannot vectorise.
