spectral-norm benchmark N=550

Each chart bar shows how many times slower, one ↓ spectral-norm program was, compared to the fastest program.

These are not the only programs that could be written. These are not the only compilers and interpreters. These are not the only programming languages.

Column × shows how many times more each program used compared to the benchmark program that used least.

    sort sortsort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0Python 2 #2 0.080.08?394  0% 13% 0% 89%
1.0Python 2 #2 0.080.08?394  0% 0% 11% 100%
1.6Nuitka #2 0.130.13?394  0% 0% 100% 0%
1.6Nuitka #2 0.130.13?394  0% 100% 0% 0%
1.6Nuitka #2 0.130.13?394  0% 0% 100% 0%
1.7Python 2 #2 0.090.14?394  7% 29% 67% 7%
2.0Python 3 #2 0.170.17?394  100% 12% 6% 0%
2.1Python 3 #2 0.170.17?394  94% 6% 6% 0%
2.2Python 3 #2 0.180.18?394  95% 17% 21% 6%
2.4PyPy 2 #8 0.200.20?594  100% 19% 10% 5%
2.5PyPy 2 #8 0.200.20?594  37% 10% 5% 64%
2.5PyPy 3 #8 0.200.20?594  100% 5% 0% 0%
2.6PyPy 3 #8 0.200.211,140594  5% 100% 5% 0%
2.6PyPy 3 #8 0.200.211,120594  9% 95% 0% 0%
2.6PyPy 2 #8 0.210.211,112594  100% 73% 33% 15%
3.0PyPy 2 #6 0.230.2421,304498  0% 100% 20% 4%
3.0PyPy 2 #6 0.240.241,116498  63% 8% 43% 12%
3.2PyPy 2 #6 0.250.261,124498  35% 73% 11% 14%
3.8PyPy 3 #6 0.310.311,124498  0% 3% 100% 0%
4.0PyPy 3 #6 0.310.321,116498  6% 100% 13% 3%
4.2PyPy 3 #6 0.310.341,140498  24% 73% 6% 3%
5.2Nuitka #3 0.420.4235,928642  100% 0% 2% 0%
5.2Pyston #2 0.420.4255,652394  2% 0% 0% 100%
5.2Pyston #2 0.420.4256,036394  0% 0% 0% 100%
5.2Nuitka #3 0.420.4230,432642  5% 2% 100% 5%
5.2Pyston #2 0.420.4257,532394  0% 10% 83% 12%
5.3Nuitka #3 0.430.4327,104642  100% 0% 0% 2%
5.8Python 3 #3 0.470.4741,308642  4% 4% 9% 100%
5.8Python 3 #3 0.470.4739,464642  2% 8% 4% 100%
6.1Python 3 #3 0.490.4939,228642  98% 4% 6% 0%
7.9Graal #8 0.850.64299,944594  11% 10% 19% 98%
8.0Graal #8 0.850.65298,652594  11% 16% 98% 12%
8.0Graal #8 0.850.65299,240594  97% 19% 11% 16%
10PyPy 2 #5 1.470.84314,504595  47% 49% 67% 45%
11Numba 0.910.9184,284663  0% 0% 0% 100%
11PyPy 2 #5 1.570.9188,928595  76% 55% 51% 47%
11Numba 0.920.9284,664663  0% 3% 49% 48%
11Numba 0.930.9383,916663  1% 0% 0% 99%
13PyPy 2 #5 1.661.0188,640595  60% 68% 66% 71%
17PyPy 3 #5 2.421.3785,080575  38% 48% 68% 40%
17PyPy 3 #5 2.451.4186,212575  40% 45% 37% 66%
18Pyston #5 4.751.43158,316595  86% 81% 85% 82%
18PyPy 3 #5 2.481.4885,576575  38% 67% 37% 41%
18Pyston #5 4.791.49158,188595  88% 79% 76% 79%
19Pyston #5 4.821.50158,392595  88% 79% 76% 77%
21Pyston #8 1.691.6927,440594  100% 1% 0% 0%
21Pyston #8 1.691.6927,568594  0% 100% 0% 0%
21Pyston #8 1.731.7327,432594  1% 0% 2% 98%
22Nuitka #5 6.501.7857,680575  93% 91% 92% 93%
23Python 2 #5 6.381.8441,668595  90% 93% 91% 92%
23Nuitka #5 6.581.8458,556575  90% 93% 90% 91%
23Python 2 #5 6.451.8941,868595  95% 91% 92% 93%
24Nuitka #5 6.621.9257,920575  87% 86% 87% 92%
24Python 2 #5 6.341.9541,648595  92% 92% 91% 93%
27Pyston #6 2.202.2127,500498  0% 0% 100% 0%
27Pyston #6 2.212.2127,340498  2% 98% 0% 0%
28Python 3 #5 7.982.2453,856575  92% 92% 92% 96%
28Python 3 #5 7.902.2854,080575  91% 93% 91% 90%
29Pyston #6 2.332.3327,464498  0% 100% 0% 0%
29Python 3 #5 7.952.3754,024575  93% 93% 92% 90%
30Python development version #5 8.642.4548,248575  92% 92% 91% 90%
30Python development version #5 8.712.4648,168575  93% 90% 93% 89%
31Python development version #5 8.802.4848,304575  92% 92% 89% 92%
35Python 2 #6 2.802.806,720498  1% 3% 1% 100%
35Python 2 #6 2.802.806,664498  2% 2% 100% 2%
35Python 2 #6 2.812.826,724498  2% 1% 100% 1%
36Nuitka #8 2.912.9110,004594  2% 100% 2% 2%
36Nuitka #6 2.942.9410,344498  3% 100% 2% 1%
36Nuitka #8 2.952.959,872594  100% 1% 0% 1%
36Nuitka #6 2.952.9510,344498  1% 100% 1% 2%
37Nuitka #6 2.962.9610,220498  1% 75% 2% 27%
37Nuitka #8 2.972.989,856594  86% 2% 14% 2%
39Python 2 #8 3.123.126,864594  1% 2% 100% 1%
39Python 2 #8 3.153.156,932594  2% 3% 1% 100%
39Python 2 #8 3.163.166,796594  2% 1% 100% 1%
43Python 3 #6 3.453.458,752498  3% 9% 100% 2%
44Python development version #6 3.543.548,012498  5% 11% 1% 90%
44Python 3 #6 3.423.588,752498  4% 100% 2% 1%
44Python development version #6 3.603.607,876498  5% 1% 100% 1%
45Python 3 #6 3.463.618,896498  6% 100% 1% 1%
46Python development version #6 3.543.698,064498  95% 1% 6% 1%
51IronPython #8 3.984.1657,520594  0% 63% 8% 24%
52IronPython #8 3.994.1861,472594  92% 0% 1% 1%
52IronPython #8 4.004.1957,200594  0% 92% 1% 1%
52Python 3 #8 4.194.208,908594  92% 12% 3% 10%
52Python development version #8 4.234.247,928594  5% 100% 1% 1%
53Python development version #8 4.254.267,988594  6% 1% 1% 100%
53Python development version #8 4.284.297,820594  5% 0% 100% 1%
53Python 3 #8 4.194.328,728594  3% 76% 4% 28%
54Python 3 #8 4.334.348,764594  14% 13% 91% 6%
57IronPython #6 4.344.5974,580498  1% 2% 13% 79%
57IronPython #6 4.354.6170,740498  7% 57% 1% 29%
57IronPython #6 4.454.6271,112498  46% 1% 48% 1%
59Jython #8 8.084.77294,356594  43% 34% 60% 31%
60Jython #8 8.374.87297,264594  51% 45% 38% 38%
61Jython #8 8.414.96294,976594  31% 64% 46% 29%
63Graal #6 9.805.14596,620498  56% 85% 53% 5%
70Graal #6 11.365.66623,692498  62% 68% 14% 64%
73Graal #6 11.765.88628,644498  6% 66% 80% 55%
84Jython #6 9.576.83284,740498  43% 37% 27% 33%
85Jython #6 9.826.89290,672498  30% 38% 31% 44%
85Jython #6 9.966.90294,640498  31% 36% 56% 21%
89MicroPython #6 7.237.254,472498  47% 8% 56% 6%
90MicroPython #6 7.207.304,472498  58% 34% 4% 16%
90MicroPython #6 7.297.314,504498  100% 9% 3% 3%
missing benchmark programs
Cython No program
Shedskin No program
Grumpy No program

 spectral-norm benchmark : Eigenvalue using the power method

diff program output N = 100 with this output file to check your program is correct before contributing.

We are trying to show the performance of various programming language implementations - so we ask that contributed programs not only give the correct result, but also use the same algorithm to calculate that result.

Each program should calculate the spectral norm of an infinite matrix A, with entries a11=1, a12=1/2, a21=1/3, a13=1/4, a22=1/5, a31=1/6, etc

For more information see challenge #3 in Eric W. Weisstein, "Hundred-Dollar, Hundred-Digit Challenge Problems" and "Spectral Norm".

From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/Hundred-DollarHundred-DigitChallengeProblems.html
http://mathworld.wolfram.com/SpectralNorm.html

Thanks to Sebastien Loisel for this benchmark.

Revised BSD license

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