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Hello, I have taken someone else's code which had several for-loops in it, and vectorized the code. The code now runs extremelly fast and even faster when using gpuArray(). I'm at a point now where the slowest part of the code is where I used the accumarray function. I'm not sure if there are alternative approaches to vectorizing code without using accum array.

Here is a rough example of the code I vectorized and an exampe of that code, vectorized

nx = 16;

nz = 512;

A = rand(nx,nx,nz);

%% First Method For Loops

R1 = zeros(nx,nx,nz);

B1 = zeros(nx,nz);

N1 = zeros(nx,nz);

for k = 1:nz

for j = 1:nx

for i = 1:nx

r = round((i^2+j^2)^0.5);

if(r <= nx)

R1(i,j,k) = r;

N1(R1(i,j,k),k) = N1(R1(i,j,k),k)+1;

B1(R1(i,j,k),k) = B1(R1(i,j,k),k) + A(i,j,k);

end

end

end

N1(1,k) = 1;

end

B1 = B1./N1;

%% Second Method Vectorized

[I,J] = ndgrid(1:nx,1:nx);

r = (I.^2+J.^2).^0.5;

R2 = round(r);

L21 = (R2 <= nx);

N2 = squeeze(repmat(histcounts(L21.*R2(:,:,1),1:nx+1),[1 1 nz]));

A2 = reshape(A,[1,numel(A)]);

B2 = reshape(repmat(L21.*R2,[1 1 nz]),[1,numel(A)]);

L22 = logical(B2).*repelem(0:nx:(nz-1)*nx,(nx)^2);

B2 = B2+L22;

B2 = reshape(accumarray((B2+logical(~B2)).',A2.*logical(B2)).',[nx nz])./N2;

% Check that Vectorized Code = For Loop Code

sum(sum(sum(B1-B2)))

The vectorized code isn't much faster when using gpuArray() and is only a tiny bit faster than the non-vectorized code without using gpuArray(). It is currently the bottleneck within my code and I'm not sure what other functions might be able to help me out here.

The great thing about the vectorized code is that I can put it into a function and pull several of the variables out of the function in an initialize step. Still though, the accumarray takes a significant amount of time to run compared to all other parts of my code (not shown) that is vectorized. And when accumarray is put into a function, is appears to be slower when I put tic/toc just outside of the function.

Thanks!

Andrea Picciau
on 20 Apr 2020

Edited: Andrea Picciau
on 20 Apr 2020

Hi Nathan,

Accumulation operations require synchronisation and are never going to squeeze the best performance out of your GPU. This is valid for all parallel computer architectures, by the way.

As a way to help myself understand your code, I rewrote the last line:

function tempData = iOriginalCode(A, B)

% Breakdown of original code

logicalB = logical(B);

indexes = B + ~logicalB;

indexes = indexes.';

values = A.*logicalB;

values = values.';

tempData = accumarray(indexes, values);

end

I can't immediately see how to change this code to improve performance and the rest of your vectorized code is not readable enough. I'm 90% sure the right thing to do is to go back to designing your algorithm to avoid that accumarray... you should take some of these things into consideration:

Try not to think in terms of indexes and values. Instead, try to formulate as many things as possible in terms of mathematical operations. You're already doing this in some parts of your code. For example, you're writing

A(~logicalB)=0

as

A.*logicalB

(see my re-written code). Eventually, you might be able re-write your accumarray as a matrix-vector multiplication, which should help improve performance.

Use the structure in your data to simplify your code. You're using a lot of repmats, reshapes, and a repelem to generate your indexes array. You can most likely find a better way to re-write that for-loop-based code.

I would go back to the original code and think again about the structure of this R

j = 1:nx;

i = 1:nx;

r = (i^2+j^2)^0.5;

R = round(r);

R(R>nx/2) = 0;

Use gputimeit to measure your GPU performance. The right way to measure the performance of the GPU on this is to use gputimeit, and on my K40c (with the data obtained from your script) I got this:

>> gputimeit(@() iOriginalCode(A, B), 1)

ans =

0.1309

The profiler can help you, but it's going to mess with the underlying flow of operations on the GPU. By the way, the profiler agrees that accumarray is the bottleneck here.

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