Maple Benchmarktest
(Version 5.0)
<Text-field style="Heading 1" layout="Heading 1">Initialisation</Text-field>restart;
with(LinearAlgebra):
with(DiscreteTransforms):
with(combinat, fibonacci):
with(Statistics):
Seed=randomize():QyQtSStjdXJyZW50ZGlyRzYiNiNROUQ6XFxNYXRoZW1hdGlrXFxOY3J1bmNoNUYlISIi
<Text-field style="Heading 1" layout="Heading 1">Misc. operations</Text-field>
<Text-field style="Heading 2" layout="Heading 2">IO Test & descriptive statistics</Text-field>LimitsArea:={1,261,522,784,1045,1305,1565,1827,2088,2349,2610,2871,3131,3158}:
t:=time():
datmat:=convert(readdata("Currency2.txt",[integer$4,float$38]),Matrix):
for k from 1 by 1 to 2000 do
for j from 1 by 1 to 13 do
YearData:=datmat[LimitsArea[j]..LimitsArea[j+1]-1,5..38]:
amountrows:=LimitsArea[j+1]-LimitsArea[j]:
for i from 1 by 1 to 34 do
YD:=YearData[1..amountrows,i]:
MinYear,MaxYear,MeanYear:=rtable_scanblock(YD,[rtable_dims(YD)],'Minimum','Maximum','Average');
GainLossYear:=100-(YearData[1,i]+0.00000001)/(YearData[amountrows,i]+0.00000001)*100:
end do:
end do:
end do:
time()-t;
<Text-field style="Heading 2" layout="Heading 2">Loop 15000x15000</Text-field>a:=1:
t:=time():
for x from 1 by 1 to 15000 do
for y from 1 by 1 to 15000 do
a:=a+x+y:
end do:
end do:
time()-t;
<Text-field style="Heading 2" layout="Heading 2">2000x2000 normal distributed random matrix^1000</Text-field>TotalTime:=0:
for i from 1 by 1 to 200 do
a:=RandomMatrix(2000,outputoptions=[datatype=float[8]],generator=1.0..1.01):
t:=time():
(Array(a)^1000):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 2" layout="Heading 2">1000000 values sorted ascending</Text-field>TotalTime:=0:
for i from 1 by 1 to 100 do
L := [seq(rand(0..1),i=1..1000000)]:
t:=time():
sort(L):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 1" layout="Heading 1">Analysis</Text-field>
<Text-field style="Heading 2" layout="Heading 2">FFT over 1048576 values</Text-field>m:=20:
TotalTime:=0:
for i from 1 by 1 to 100 do
x:=RandomVector[row](2^m,generator=0.0..1.0,outputoptions=[datatype=float[8]]):
t:=time():
FourierTransform(x):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 1" layout="Heading 1">Algebra</Text-field>
<Text-field style="Heading 2" layout="Heading 2">Determinant of a 1500x1500 random matrix</Text-field>TotalTime:=0:
for i from 1 by 1 to 100 do
a:=RandomMatrix(1500,outputoptions=[datatype=float[8]],generator=0.0..1.0):
t:=time():
Determinant(a):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 2" layout="Heading 2">Inverse of a 1500x1500 uniform distr. random matrix</Text-field>TotalTime:=0:
for i from 1 by 1 to 100 do
a:=RandomMatrix(1500,outputoptions=[datatype=float[8]],generator=0.0..1.0):
t:=time():
MatrixInverse(a):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 2" layout="Heading 2">Eigenvalues of a normal distr. 1200x1200 randommatrix</Text-field>a:=RandomMatrix(1200,outputoptions=[datatype=float[8]],generator=0.0..1.0):time(Eigenvalues(a));
<Text-field style="Heading 2" layout="Heading 2">Cholesky decomposition of a 1500x1500-matrix</Text-field>TotalTime:=0:
for i from 1 by 1 to 100 do
S:=RandomMatrix(1500,outputoptions=[datatype=float[8]],generator=0.0..1.0):
A := Transpose(S).S:
t:=time():
LUDecomposition(A,method='Cholesky'):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 2" layout="Heading 2">1500x1500 cross-product matrix</Text-field>TotalTime:=0:
for i from 1 by 1 to 100 do
S:=RandomMatrix(1500,outputoptions=[datatype=float[8]],generator=0.0..1.0):
t:=time():
Transpose(S).S:
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 1" layout="Heading 1">Number theory</Text-field>
<Text-field style="Heading 2" layout="Heading 2">Calculation of 10000000 fibonacci numbers</Text-field>frnd:=rand(100..1000):
A := [seq(frnd(),i=1..10000000)]:time(evalf(Map(a->fibonacci(a), A)));
<Text-field style="Heading 1" layout="Heading 1">Stochastic-statistic</Text-field>
<Text-field style="Heading 2" layout="Heading 2">Principal component analysis over a 10000x1000 matrix</Text-field>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
<Text-field style="Heading 2" layout="Heading 2">Gamma function over a 1500x1500 matrix</Text-field>TotalTime:=0:
for i from 1 by 1 to 100 do
a:=RandomMatrix(1500,generator=0.01..1.0,outputoptions=[datatype=float[8]]):
t:=time():
Map(GAMMA,a):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 2" layout="Heading 2">Gaussian error function over a 1500x1500 matrix</Text-field>TotalTime:=0:
for i from 1 by 1 to 100 do
a:=RandomMatrix(1500,density=0.5,generator=0.0..1.0,outputoptions=[datatype=float[8]]):
t:=time():
Map(erf,a):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);
<Text-field style="Heading 2" layout="Heading 2">Linear regression over a 1000x1000 matrix</Text-field>TotalTime:=0:
for i from 1 by 1 to 100 do
A:=RandomMatrix(1000,density=0.5,generator=0.0..1.0,outputoptions=[datatype=float[8]]):
B:=RandomVector(1000,density=0.5,generator=0.0..1.0,outputoptions=[datatype=float[8]]):
t:=time():
LeastSquares(A,B):
TotalTime:=TotalTime+time()-t:
end do:
print(TotalTime);JSFH