How to maximize MATLAB's GPU utility?

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John Muradeli
John Muradeli 2019-3-20
编辑： John Muradeli 2019-3-20
I've surveyed my GPU's performance against itself and the CPU for varying matrix sizes, and found the opposite of what most GPU literature suggests: the GPU's computing advantage diminishes with array size. Code, results, & specs shown below. Noteworthy observations: . (1) GPU utility remains sub-10%, according to Task Manager (2) ~(50%, 20%) = (RAM, CPU) usage for large (K > 9000) array (3) Considerable speed ratio drop's observed for around K > 8000 (4) Splitting the K > 8000 (= 9000) Xga matrix into four increases vectorized speed two-fold (5) My GPU ranks far higher among GPUs than my CPU (#24 vs. #174); it thus seems an on-par CPU would outperform the GPU for larger arrays (6) Last pic's GPU vs. CPU benchmark supports (5); GPU isn't as vastly superior as expected
What's the culprit - is my code, or MATLAB, or hardware configuration under-utilizing the GPU? How to find out and resolve it? m-files: testrun.zip (testrun compares performance for a single K; testrun0 for multiple)
%% CODE: centroid indexing in K-means algorithm
% size(X) = [16000, 3]
% size(c) = [K, 3]
% Xsg = single(X); csg = single(c);
% Xga = gpuArray(Xsg); cga = gpuArray(csg);
% Speed ratio = t2/t1, if t2 > t1 - else, t1/t2
%% TIMING
f1 = fasterFunction(...); % e.g. vectorized(Xga, cga, K, m)
f2 = slowerFunction(...); % e.g. forVectorized(X, c, m)
t1 = gputimeit(f1) % OR timeit(f1) for non-GPU arrays
t2 = timeit(f2) % OR gputimeit(f2) for GPU arrays
%% FUNCTIONS
function out = vectorized(X, c, K, m)
[~, out] = min(reshape(permute(sum((X-permute(c,[3 2 1])).^2,2), ...
[1 2 3]),m,K),[],2);
end
function out = forVectorized(X, c, m)
out = zeros(m,1);
for j=1:m
[~,out(j)] = min(sum(((X(j,:))'-c').^2));
end
end
function out = forFor(X,c,K,m)
out = zeros(m,1); idxtemp = zeros(K,1);
for i=1:m
for j=1:K
idxtemp(j) = sum((X(i,:)-c(j,:)).^2,2);
end
[~, out(i)] = min(idxtemp);
end
end
%% PLOTS
% GPU vectorized = vectorized(Xga, cga, K, m) for varying K, timed w/ gputimeit
% CPU vectorized = vectorized(Xsg, csg, K, m) for varying K, timed w/ timeit
% for-loop = forFor(Xsg, csg, K, m) for varying K, timed w/ timeit
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John Muradeli
John Muradeli 2019-3-20
@Jan -- Unsure how columns/rows affect CPU computing, but - transposed per your suggestion, and interchanged (i,:) with (i,:) (same w/ j) - results: https://puu.sh/D2Lex/ea9c4d6189.png -- not a significant difference for range of K's tested
John Muradeli
John Muradeli 2019-3-20
编辑：John Muradeli 2019-3-20
@Jan: Very well, I'll clarify below; as for the complete code - there's a tradeoff between conciseness and thoroughness - too much of the latter tends to throw off readers the fastest. This said, would an m-file suffice? The code isn't brief.
"Maximize GPU Utility" - see (1), (2); that is, it seems that majority of GPU resources aren't being utiilzied - and that there may be a way to utilzie them. For example, dividing workload evenly across the entire GPU - rather than have a few take all and most lay idle. I tried one method (see (4)); but strangely, for K <= 8000, computing time increases. Hence, I may be doing it wrong.
@"Doesn't the last diagram tell the opposite?" it's not so much GPU vs CPU as GPU vs GPU: performance slightly decreases after peak (circled) - but not as much as in plots above. I couldn't test for 1e9 per 'Out of Memory'

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