Acceleration of code by replacing two nested for-loops
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Hello,
I am trying to accelerate my code, where I calculate the matrix containing the output image 'Out' by using two nested for loops as part of a simulation for imaging with a spatially varying point spread function. Basically, 'Out' is the sum of each pixel (ix,iy) of the input image 'A' multiplied with an image 'B(:,:,ix,iy)' corresponding to the point spread function of that pixel. The code to calculate that looks something like this (I defined A and B as zeros here just to show their size):
x_max = 256;
y_max = 256;
A = zeros(x_max,y_max);
B = zeros(x_max,y_max,x_max,y_max);
Out = zeros(x_max,y_max);
for ix=1:x_max
for iy=1:y_max
Out = Out + A(ix,iy)*B(:,:,ix,iy);
end
end
My question is, can this code be accerated by replacing these nested for-loops by using matrix multiplication?
Many thanks,
Tom
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Answers (1)
Star Strider
on 29 Feb 2024
This could be difficult with your code because of the multidimensional nature of ‘B’, however the usual approach is to create matrices for the different arguments and then use those in the function, rather than creating nested loops.
To illustrate —
x = linspace(-5, 5, 20);
y = linspace(0, 10, 30);
z = @(x,y) exp(-(x.^2/10 + (y-5).^2/5));
[X, Y] = ndgrid(x,y);
Z = z(X,Y)*5; % No (Explicit) Loops!
figure
surf(X, Y, Z)
colormap(turbo)
colorbar
xlabel('X')
ylabel('Y')
axis('equal')
.
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