The Flop (Floating Point Operations per Second) Rate of MATLAB Code
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Hello, I know Intel MKL / IPP libraries performance in simple operations (Multiplication, Summation, Matrix Multiplication, Vector Multiplication) gets something like 80-95% of the theoretical performance of the CPU (Measured in FLOPS).
http://software.intel.com/en-us/articles/parallelism-in-the-intel-math-kernel-library http://software.intel.com/en-us/intel-mkl
Yet, doing so using MATLAB I get much worse results.
I have this simple script:
numElements = 2 ^ 16;
numIter = 100;
vecX = randn(numElements, 1, 'single');
vecY = randn(numElements, 1, 'single');
initTime = tic();
for ii = 1:numIter
vecX .* vecY;
end
stopTime = toc(initTime);
gFlops = (numElements * numIter) / stopTime
Yet I get only 1.1 GFLOPS on my i7-860 Which should be closer to 2.8GHz (Frequency) * 4 (Cores) * 4 (Single Precisio Operations per Cycle as SSE Vector - 128 Bit) = 44.8 GFLOPS.
Yet I get something like 1.4 GFLOPS. Which is only 3% of the theoretical performance.
How can MATLAB be so inefficient?
2 commentaires
Amit
le 28 Jan 2014
BTW, MATLAB is only using 1 core, I'd believe. And for a benchmark, is there anything else running besides MATLAB?
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Amit
le 28 Jan 2014
Modifié(e) : Amit
le 28 Jan 2014
I don't think the way you're trying to calculate flops here is right. Even if one assumes that you can calculate Flops like this, you're missing out many overheads that matlab is doing. For example, try something like this:
numElements = 2 ^ 18;
numIter = 100;
vecX = randn(numElements, 1, 'single');
vecY = randn(numElements, 1, 'single');
initTime = tic();
% for ii = 1:numIter
% vecX .* vecY;
% end
vecX + vecY; % I used +, but you can switch to .* as well
stopTime = toc(initTime);
gFlops = (numElements * numIter) / stopTime
And see if you see any difference. I am pretty sure you will. Remember, for loop is slow.
4 commentaires
Walter Roberson
le 16 Juin 2019
tic and toc only provide elapsed time information, which is not the same as the amount of computation done, as elapsed time can include time that the operating system suspended MATLAB in order to work on something else.
Plus de réponses (1)
Walter Roberson
le 28 Jan 2014
single() is often slower than double()
Your arrays are not that big; I am not sure that it is kicking in calls to the libraries.
2 commentaires
Walter Roberson
le 29 Jan 2014
Try with timeit. Or if you have an older MATLAB that does not have that built-in, you can get timeit from the File Exchange.
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