How can I use the MATLAB vector processing instead of this for loop?
2 views (last 30 days)
Show older comments
Hi guys,
does anybody have an idea how I could speed up the following loop:
Havg = zeros(1, length(Gsum));
for k=1:K
Havg = Havg + circshift(Gsum, [0 numBlocks*M*(k-1)]);
end
Here, length(Gsum)=280000 and K=1200. NumBlocks equals 10 and M=14. This loop is very slow. However, I did not find a way to use the MATLAB-specific vector processing to speed it up.
How can I speed up the loop, e.g. with MATLABs powerful vector processing?
Regards, Max
2 Comments
Accepted Answer
Matt J
on 11 Dec 2012
If Gsum is sparse, there may be better ways than the following fft-based method:
numBlocks=10;
M=14;
N=length(Gsum);
K=1200;
f=@(t) mod(t-1,N)+1;
shifts = f(1:numBlocks*M:numBlocks*M*(K-1)+1);
comb=accumarray(shifts.',1,[N,1]).';
Havg=ifft(fft(Gsum).*fft(comb),'symmetric');
More Answers (1)
Roger Stafford
on 11 Dec 2012
As your code is now, you are performing 280,000 X 1,200 = 336,000,000 additions. You can cut down the number of flops by a factor of about 1/333 with the use of matlab's 'cumsum' function.
p = 140; q = 2000; r = 1200;
Havg = reshape(Gsum,p,q);
Havg = cumsum([zeros(p,1),Havg,Havg(:,1:r-1)],2);
Havg = reshape(Havg(:,r+1:r+q)-Havg(:,1:q),1,[]);
It should be noted, however, that the above reduction in the number of flops comes at the cost of increased round-off error accumulated by 'cumsum' over q+r = 3200 steps in each of the 140 rows in the next-to-last line.
Roger Stafford
0 Comments
See Also
Categories
Find more on Loops and Conditional Statements in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!