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 0.070.07?394  0% 0% 0% 100%
1.0Python 2 #2 0.070.07?394  0% 0% 86% 0%
1.0Python 2 #2 0.070.07?394  13% 0% 57% 43%
1.3PyPy 2 #8 0.080.09?594  89% 0% 0% 0%
1.3PyPy 2 #8 0.080.09?594  0% 0% 0% 89%
1.4PyPy 3 #8 0.090.09?594  100% 0% 0% 0%
1.4PyPy 3 #8 0.090.09?594  67% 0% 33% 0%
1.4PyPy 3 #8 0.090.09?594  0% 100% 0% 0%
1.7Python 3 #2 0.120.12?394  0% 0% 100% 8%
1.7Python 3 #2 0.120.12?394  0% 0% 0% 100%
1.8Python 3 #2 0.120.12?394  9% 82% 0% 8%
2.3PyPy 2 #8 0.150.15?594  0% 64% 6% 33%
2.5PyPy 2 #2 0.170.17?394  88% 0% 0% 17%
2.5PyPy 2 #2 0.170.17?394  0% 100% 0% 0%
2.5PyPy 2 #2 0.170.17?394  0% 0% 0% 100%
2.6PyPy 2 #6 0.180.18?498  0% 0% 0% 100%
2.6PyPy 2 #6 0.180.18?498  0% 0% 100% 0%
3.2PyPy 2 #6 0.210.221,492498  0% 95% 0% 5%
3.7PyPy 3 #6 0.250.251,356498  4% 100% 0% 4%
3.7PyPy 3 #6 0.250.251,332498  0% 0% 100% 0%
3.7PyPy 3 #6 0.250.251,328498  0% 0% 12% 88%
4.5Python 3 #3 0.300.301,836642  0% 0% 100% 3%
4.8Python 3 #3 0.320.322,004642  72% 3% 6% 19%
5.0Nuitka #3 0.330.331,660642  100% 0% 3% 0%
5.0Nuitka #3 0.340.341,672642  3% 100% 0% 0%
5.1Python 3 #3 0.340.341,816642  100% 0% 0% 3%
5.2PyPy 3 #2 0.350.351,328394  0% 3% 3% 97%
5.2PyPy 3 #2 0.350.351,348394  97% 0% 0% 0%
5.5Nuitka #3 0.360.371,756642  0% 0% 25% 75%
6.1PyPy 3 #2 0.410.4172,876394  0% 0% 15% 86%
6.3Pyston #2 0.420.4255,652394  2% 0% 0% 100%
6.3Pyston #2 0.420.4256,036394  0% 0% 0% 100%
6.3Pyston #2 0.420.4257,532394  0% 10% 83% 12%
8.9PyPy 2 #5 1.190.60282,704595  49% 42% 58% 48%
9.0PyPy 2 #5 1.210.61279,996595  47% 44% 54% 53%
9.2PyPy 2 #5 1.190.61281,308595  44% 48% 46% 56%
9.7PyPy 3 #3 0.650.65153,836642  0% 0% 0% 100%
9.9PyPy 3 #3 0.660.66143,380642  0% 4% 97% 2%
10PyPy 3 #3 0.670.67142,648642  0% 100% 1% 0%
14Numba 0.910.9184,284663  0% 0% 0% 100%
14Numba 0.920.9284,664663  0% 3% 49% 48%
14Numba 0.930.9383,916663  1% 0% 0% 99%
14PyPy 3 #5 1.900.95283,668575  45% 48% 49% 58%
15PyPy 3 #5 1.951.01283,768575  50% 41% 63% 38%
15PyPy 3 #5 1.981.02286,876575  41% 43% 67% 42%
21Pyston #5 4.751.43158,316595  86% 81% 85% 82%
22Pyston #5 4.791.49158,188595  88% 79% 76% 79%
22Pyston #5 4.821.50158,392595  88% 79% 76% 77%
25Pyston #8 1.691.6927,440594  100% 1% 0% 0%
25Pyston #8 1.691.6927,568594  0% 100% 0% 0%
26Python 2 #5 6.361.7342,396595  92% 92% 93% 91%
26Pyston #8 1.731.7327,432594  1% 0% 2% 98%
26Python 2 #5 6.321.7344,180595  91% 92% 91% 92%
26Python 2 #5 6.611.7644,380595  94% 96% 93% 94%
26Nuitka #5 6.431.7759,316575  91% 94% 91% 90%
26Nuitka #5 6.431.7759,684575  90% 93% 90% 90%
27Nuitka #5 6.621.8159,272575  90% 90% 90% 96%
32Python 3 #5 7.842.1256,248575  91% 91% 96% 92%
32Python 3 #5 7.872.1653,580575  92% 90% 93% 91%
32Python 3 #5 7.902.1756,208575  91% 91% 93% 89%
33Pyston #6 2.202.2127,500498  0% 0% 100% 0%
33Pyston #6 2.212.2127,340498  2% 98% 0% 0%
35Pyston #6 2.332.3327,464498  0% 100% 0% 0%
38Python development version #5 9.512.5649,964575  93% 93% 94% 94%
38Python development version #5 9.492.5752,260575  94% 92% 94% 94%
39Python development version #5 9.612.6150,292575  94% 93% 97% 95%
42Nuitka #8 2.822.8210,364594  100% 0% 0% 1%
42Python 2 #6 2.832.836,928498  100% 0% 0% 0%
42Python 2 #6 2.842.846,964498  0% 41% 0% 58%
42Nuitka #8 2.842.8410,580594  0% 0% 100% 0%
42Nuitka #8 2.842.8510,684594  0% 0% 100% 0%
43Python 2 #6 2.872.876,968498  100% 0% 1% 0%
44Nuitka #6 2.952.9510,660498  0% 0% 100% 0%
44Nuitka #6 2.972.9710,648498  100% 0% 0% 0%
44Nuitka #6 2.972.9710,232498  0% 0% 100% 0%
49Python 2 #8 3.253.256,956594  0% 0% 0% 100%
49Python 2 #8 3.263.266,920594  100% 0% 0% 1%
49Python 2 #8 3.273.286,976594  0% 0% 0% 100%
50Python 3 #6 3.353.358,744498  0% 1% 1% 99%
50Python 3 #6 3.373.378,768498  0% 63% 0% 37%
51Python 3 #6 3.423.429,064498  100% 0% 0% 0%
58Python development version #6 3.883.887,692498  2% 2% 100% 2%
59Python development version #6 3.943.947,484498  3% 1% 2% 100%
59Python development version #6 3.953.957,484498  12% 2% 90% 3%
61Python 3 #8 4.094.099,068594  0% 68% 0% 32%
62Python 3 #8 4.124.138,868594  0% 100% 0% 0%
62Python 3 #8 4.154.169,108594  0% 1% 99% 0%
62IronPython #8 3.984.1657,520594  0% 63% 8% 24%
62IronPython #8 3.994.1861,472594  92% 0% 1% 1%
63IronPython #8 4.004.1957,200594  0% 92% 1% 1%
68IronPython #6 4.344.5974,580498  1% 2% 13% 79%
69IronPython #6 4.354.6170,740498  7% 57% 1% 29%
69IronPython #6 4.454.6271,112498  46% 1% 48% 1%
71Jython #8 8.084.77294,356594  43% 34% 60% 31%
73Jython #8 8.374.87297,264594  51% 45% 38% 38%
74Jython #8 8.414.96294,976594  31% 64% 46% 29%
77Python development version #8 5.175.187,788594  2% 100% 2% 2%
79Python development version #8 5.275.277,756594  2% 1% 2% 99%
81Python development version #8 5.445.447,692594  55% 3% 47% 2%
102Jython #6 9.576.83284,740498  43% 37% 27% 33%
103MicroPython #6 6.876.874,312498  100% 0% 0% 0%
103MicroPython #6 6.876.884,352498  0% 67% 0% 34%
103Jython #6 9.826.89290,672498  30% 38% 31% 44%
103Jython #6 9.966.90294,640498  31% 36% 56% 21%
103MicroPython #6 6.906.904,196498  0% 92% 0% 8%
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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