How can I speed up this function? #vectorize
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I'm struggling to speed up the code inside the version1 function. The only way to vectorize it that I can seem to figure out is version2, but that actually makes the function slower. Help would be much appreciated :-)
n1 = 400;
n2 = 3;
n3 = 7;
n4 = 2;
A = rand(n1, n2, n3);
B = randi(n2, [n4, n1, n3]);
disp(timeit(@() version1(A, B, C, n1, n2, n3, n4)));
disp(timeit(@() version2(A, B, C, n1, n2, n3, n4)));
function C = version1(A, B, n1, n2, n3, n4)
C = zeros(n4, n1, n3);
for ii = 1:n3
for jj = 1:n1
for kk = 1:n4
C(kk, jj, ii) = A( ...
jj, ...
B(kk, jj, ii), ...
ii ...
);
end
end
end
end
function C = version2(A, B, n1, n2, n3, n4)
C = zeros(n4, n1, n3);
for ii = 1:n3
for jj = 1:n1
C(:, jj, ii) = A( ...
jj, ...
B(:, jj, ii), ...
ii ...
);
end
end
end
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采纳的回答
Matt J
2024-1-12
编辑:Matt J
2024-1-13
I doubt a vectorized solution would be faster than the loop, but here is one way to vectorize it.
[K,J,I]=ndgrid(1:n4,1:n1,1:n3); %Recycle this, if possible
Bvals=B(sub2ind(size(B),K,J,I));
C=A( sub2ind(size(A), J,Bvals,I ));
4 个评论
Matt J
2024-1-13
编辑:Matt J
2024-1-13
No, I would do this instead:
function C = version3(A, B, n1, n2, n3, n4)
[K,J,I]=ndgrid(1:n4,1:n1,1:n3); %Recycle this, if possible
Bvals=B(sub2ind(size(B),K,J,I));
C=A( sub2ind(size(A), J,Bvals,I ));
end
It doesn't matter though. Surely you've found by now that the for-loop is fastest.
更多回答(1 个)
Hassaan
2024-1-12
It seems that the complexity of the indexing in this specific case is not easily amenable to vectorization without encountering shape mismatches or broadcasting issues.it might be more practical to focus on optimizing the original loop-based implementations (version1 and version2). Often, the simplest solution can be the most efficient, especially if the code is already quite optimized and the overhead of additional complexity does not lead to significant performance gains.
If performance is critical, and these functions are a bottleneck in your application, you might consider other strategies, such as:
- Profiling: Use MATLAB's built-in profiling tools to identify the exact bottlenecks in your code.
- Parallel Computing: If you have access to MATLAB's Parallel Computing Toolbox, you might gain performance by distributing some of these operations across multiple cores or GPUs.
- Compiled Languages: For the most intensive computational tasks, rewriting the critical parts in a compiled language like C++ and integrating it with MATLAB might be beneficial.
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