This is your Julia code. The result: 970 microSeconds (@benchmark answer), 1550 microSeconds, 1090 microSeconds.
This is for a big matrix with the size of 500. If you decrease the matrix size, the noise will be much more!
In your validation example, you combined three operations like addition, svd, and eigen so the noise gets less apparent.
You are using seconds as the unit and linear plots, and that’s why you don’t notice the noise.
using BenchmarkTools;
using LinearAlgebra;
using Statistics;
function SimpleMatrixOperation( mX , mY )
mA = (0.2 .* mX) .+ (0.6 .* mY);
end
function MeasureTimeIterations( mX , mY, numIterations )
vRunTime = zeros(numIterations);
for ii = 1:numIterations
vRunTime[ii] = @elapsed begin
mA = SimpleMatrixOperation(mX , mY);
end
end
runTime = minimum(vRunTime);
return runTime;
end
function MeasureTimeBelapsed( mX , mY )
runTime = @belapsed SimpleMatrixOperation(mX , mY);
return runTime;
end
function MeasurTimeBenchmark(mX , mY)
runTime = @benchmark SimpleMatrixOperation(mX , mY)
end
function directMeasurement( mX , mY)
runTime = @elapsed begin
mA = (0.2 .* mX) .+ (0.6 .* mY);
end
return runTime
end
function forDirectMeasurement(mX , mY)
vRunTime=Array{Float64}(undef,numIterations)
for ii = 1:numIterations
vRunTime[ii] = directMeasurement(mX , mY);
end
runTime = minimum(vRunTime);
end
const matrixSize = 500;
const numIterations = 7;
const mX = randn(matrixSize, matrixSize);
const mY = randn(matrixSize, matrixSize);
runTimeA = MeasureTimeIterations(mX , mY, numIterations);
# runTimeB = MeasureTimeIterations(mX , mY, 10 * numIterations);
runTimeD = MeasurTimeBenchmark(mX , mY);
runTimeE = forDirectMeasurement(mX , mY)
# @btime SimpleMatrixOperation($mX , $mY)
@benchmark SimpleMatrixOperation($mX , $mY)
But this is for Julia which has a very good compiler. For Matlab, it is even worse. See the big flactuation in the direct measurement result!
% validation
matrixSize=500;
numIterations=7; % 700
mX = randn(matrixSize, matrixSize);
mY = randn(matrixSize, matrixSize);
%%
mRunTime=zeros(1,numIterations);
for kk = 1:numIterations
fun=@() SimpleMatrixOperation(mX, mY);
mRunTime(kk) = timeit(fun)*1e6; % computes median of bench times
end
timeItResult=min(mRunTime);
disp(timeItResult)
%%
mRunTime=zeros(1,numIterations);
for kk = 1:numIterations
[mA, mRunTime(kk)]=DirectSimpleMatrixOperation( mX , mY );
end
directResult=min(mRunTime);
disp(directResult)
%%
function [mA, runtime]=DirectSimpleMatrixOperation( mX , mY )
tic();
mA = (0.2 * mX) + (0.6 * mY);
runtime=toc();
end
%%
function mA=SimpleMatrixOperation( mX , mY )
mA = (0.2 * mX) + (0.6 * mY);
end
% using Numiterations 7:
% Results for different runs:
>> validation
642.38
530.5
>> validation
648.18
721.6
>> validation
647.03
583.9
% using numIterations 700:
>> validation
642.2
457.5
>> validation
644.03
437
Also, median is a better statistical measure than minimum, since it shows what happens most of the time.