CODEWITHSUNDEEP
performancematlabfor-loopvectorization
DavideM
DavideM

Reputation: 106

Vectorization or alternative to speed up MATLAB loop

I am using MATLAB to run a for loop in which variable-length portions of a large vector are updated at each iteration with the content of another vector; something like:

for k=1:N
   vec1(idx_start1(k):idx_end1(k)) = vec1(idx_start1(k):idx_end1(k)) +...
        a(k)*vec2(idx_start2(k):idx_end2(k));
end

The selected portions of vec1 and vec2 are not so small and N can be quite large; moreover, if this can be useful, idx_end(k)<idx_start(k+1) does not necessarily hold (i.e. vec1's edited portions may be partially re-updated in subsequent iterations). As a consequence, the above is by far the slowest portion of code in my script and I would like to speed it up, if possible.

Is there any way to vectorize the above for loop in order to make it run faster? Or, are there any alternative approaches to improve its execution speed?

EDIT:

As requested in the comments, here are some example values: Using the profiler to check execution times, the loop above runs in about 3.3 s with N=5e4, length(vec1)=3e6, length(vec2)=1.7e3 and the portions indexed by idx_start/end are slightly shorter on average than the latter, although not significantly.

Of course, 3.3 s is not particularly worrying in itself, but I would like to be able to increase especially N and vec1 by 1 or 2 orders of magnitude and in such a loop it will take quite longer to run.

Upvotes: 0

Views: 115

Answers (1)

Kenneth Boyd
Kenneth Boyd

Reputation: 667

Sorry, I couldn't find a way to speed up your code. This is the code I created to try to speed it up:

N = 5e4;
vec1 = 1:3e6;
vec2 = 1:1.7e3;
rng(0)
a = randn(N, 1);
idx_start1 = randi([1, 2.9e6], N, 1);
idx_end1 = idx_start1 + 1000;
idx_start2 = randi([1, 0.6e3], N, 1);
idx_end2 = idx_start2 + 1000;
for k=1:N
   vec1(idx_start1(k):idx_end1(k)) = vec1(idx_start1(k):idx_end1(k)) + a(k) * vec2(idx_start2(k):idx_end2(k));
%    use = idx_start1(k):idx_end1(k);
%    vec1(use) = vec1(use) + a(k) * vec2(idx_start2(k):idx_end2(k));
end

The two commented-out lines of code in the for loop were my attempt to speed it up, but it actually made it slower, much to my surprise. Generally, I would create a variable for an array that is used more than once thinking that is faster, but it is not. The code that is not commented out runs in 0.24 s versus 0.67 seconds for the code that is commented out.

Upvotes: 1

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