CODEWITHSUNDEEP
matlabfor-loopmatrixvectorization
SamuelNLP
SamuelNLP

Reputation: 4146

Vectorized code slower than for loop in Matlab

I have a matrix 8x8 called gimg. I've performed this code for 5 different gimg matrices with this code, one vectorized, the other one in a for loop.

tic
dm = zeros(size(gimg));

for x = 1:size(gimg, 1)
    for y = 1:size(gimg, 2)
        dm(x, y) = (1/(1 + (x - y)^2))*gimg(x,y);
    end
end
toc

tic
[x,y] = ndgrid(1:size(gimg, 1),1:size(gimg, 2));  

dm = (ones(size(gimg))./(1 + (x - y).^2)).*gimg;
toc

Here are the results,

Elapsed time is 0.000057 seconds.
Elapsed time is 0.000247 seconds.

Elapsed time is 0.000062 seconds.
Elapsed time is 0.000199 seconds.

Elapsed time is 0.000056 seconds.
Elapsed time is 0.000195 seconds.

Elapsed time is 0.000055 seconds.
Elapsed time is 0.000192 seconds.

Elapsed time is 0.000056 seconds.
Elapsed time is 0.000187 seconds.

Is it because of the ones matrix?

I find that the feature acceleration in matlab changes the times dramatically for for loops. So my question is, is it worth to vectorize the code now with this features from JIT compiler?

UPDATE: this is one example of my gimg matrix

gimg =

         259          42           0           0           0           0           0           0
          42        1064          41           0           0           0           0           0
           0          55        3444         196           0           0           0           0
           0           0         215        3581          47           0           0           0
           0           0           0         100         806           3           0           0
           0           0           0           0           3           2           0           0
           0           0           0           0           0           0           0           0
           0           0           0           0           0           0           0           0

UPDATE 2: results from @Divakar code

>> test_vct
------------------------ With Original Loopy Approach
Elapsed time is 5.269883 seconds.
------------------------ With Original Vectorized Approach
Elapsed time is 6.314792 seconds.
------------------------ With Proposed Vectorized Approach
Elapsed time is 3.146764 seconds.
>>

So, in my computer the original vectorized approach is still slower.

My computer specs and Matlab version

Upvotes: 3

Views: 204

Answers (1)

Divakar
Divakar

Reputation: 221774

This seems faster than both of those -

dm = (1./(1+bsxfun(@minus,[1:size(gimg, 1)]',1:size(gimg, 2)).^2).*gimg);

Benchmarking code -

%// Random input
gimg = rand(8,8);

%// Number of trials (keep this a big number, as so to get runtimes of 1sec+
num_iter = 100000;

disp('------------------------ With Original Loopy Approach')
tic
for iter = 1:num_iter
    dm = zeros(size(gimg));     
    for x = 1:size(gimg, 1)
        for y = 1:size(gimg, 2)
            dm(x, y) = (1/(1 + (x - y)^2))*gimg(x,y);
        end
    end
end
toc

disp('------------------------ With Original Vectorized Approach')
tic
for iter = 1:num_iter
    [x,y] = ndgrid(1:size(gimg, 1),1:size(gimg, 2));
    dm2 = (ones(size(gimg))./(1 + (x - y).^2)).*gimg;
end
toc

disp('------------------------ With Proposed Vectorized Approach')
tic
for iter = 1:num_iter
    dm3 = (1./(1+bsxfun(@minus,[1:size(gimg, 1)]',1:size(gimg, 2)).^2).*gimg);
end
toc

Results -

------------------------ With Original Loopy Approach
Elapsed time is 4.996531 seconds.
------------------------ With Original Vectorized Approach
Elapsed time is 2.684011 seconds.
------------------------ With Proposed Vectorized Approach
Elapsed time is 1.338118 seconds.

Upvotes: 3

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