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%
1.5Python development version #2 0.120.12?394  100% 8% 0% 0%
1.5Python development version #2 0.120.12?394  8% 100% 0% 0%
1.5Python 3 #2 0.120.12?394  8% 100% 8% 0%
1.5Python 3 #2 0.120.12?394  14% 100% 15% 0%
1.5Python development version #2 0.120.12?394  8% 8% 100% 8%
1.6PyPy 3 #8 0.130.13?594  20% 100% 0% 0%
1.6PyPy 3 #8 0.130.13?594  0% 0% 100% 0%
1.7PyPy 3 #8 0.130.13?594  93% 0% 0% 0%
1.7Python 3 #2 0.130.14?394  100% 0% 0% 8%
1.7Python 2 #2 0.090.14?394  7% 29% 67% 7%
1.7PyPy 2 #8 0.140.14?594  36% 0% 0% 100%
1.7PyPy 2 #8 0.140.14?594  7% 0% 0% 93%
1.7PyPy 2 #8 0.140.14?594  0% 100% 0% 0%
2.9PyPy 2 #6 0.230.231,588498  9% 0% 0% 96%
2.9PyPy 2 #6 0.230.231,620498  8% 100% 8% 0%
2.9PyPy 2 #6 0.230.231,484498  8% 100% 0% 0%
3.5PyPy 3 #6 0.290.291,316498  7% 0% 0% 100%
3.6PyPy 3 #6 0.290.291,308498  7% 0% 0% 97%
3.6PyPy 3 #6 0.290.291,360498  7% 0% 100% 3%
4.7Python development version #3 0.380.381,864642  0% 3% 100% 3%
4.7Python development version #3 0.380.381,820642  3% 0% 97% 0%
4.7Python development version #3 0.380.381,812642  5% 0% 100% 0%
4.8Python 3 #3 0.390.391,812642  10% 100% 5% 0%
4.8Python 3 #3 0.380.391,900642  8% 3% 5% 95%
4.9Nuitka #3 0.400.40736642  3% 8% 100% 3%
5.0Nuitka #3 0.400.40728642  5% 5% 0% 98%
5.1Nuitka #3 0.410.4132,152642  10% 5% 100% 3%
5.1Python 3 #3 0.390.4131,192642  100% 5% 5% 2%
5.2Pyston #2 0.420.4256,036394  0% 0% 0% 100%
5.2Pyston #2 0.420.4255,652394  2% 0% 0% 100%
5.2Pyston #2 0.420.4257,532394  0% 10% 83% 12%
8.4PyPy 2 #5 1.290.68313,896595  54% 42% 41% 62%
8.4PyPy 2 #5 1.260.68311,756595  62% 37% 42% 51%
8.6PyPy 2 #5 1.290.69314,244595  46% 49% 60% 40%
11Numba 0.910.9184,284663  0% 0% 0% 100%
11Numba 0.920.9284,664663  0% 3% 49% 48%
11Numba 0.930.9383,916663  1% 0% 0% 99%
15PyPy 3 #5 2.201.1986,544575  38% 37% 43% 74%
15Graal #8 1.521.19469,564594  59% 14% 17% 58%
15PyPy 3 #5 2.201.2086,116575  51% 41% 39% 64%
15Graal #8 1.521.20469,748594  65% 49% 27% 10%
15PyPy 3 #5 2.221.2085,868575  41% 71% 44% 38%
15Graal #8 1.571.23471,360594  19% 16% 8% 100%
18Pyston #5 4.751.43158,316595  86% 81% 85% 82%
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%
23Python 2 #5 6.381.8441,668595  90% 93% 91% 92%
23Nuitka #5 6.761.8656,160575  96% 93% 94% 93%
23Nuitka #5 6.751.8656,152575  94% 96% 94% 92%
23Nuitka #5 6.741.8656,108575  93% 93% 93% 96%
23Python 2 #5 6.451.8941,868595  95% 91% 92% 93%
24Python 2 #5 6.341.9541,648595  92% 92% 91% 93%
25Python 3 #5 7.592.0652,984575  95% 95% 94% 97%
25Python 3 #5 7.632.0653,464575  94% 98% 93% 96%
26Python development version #5 7.742.1252,160575  91% 91% 95% 91%
27Python development version #5 7.882.1652,148575  91% 91% 94% 92%
27Python 3 #5 7.582.1753,204575  90% 92% 91% 90%
27Python development version #5 7.662.1752,928575  92% 91% 89% 92%
27Pyston #6 2.202.2127,500498  0% 0% 100% 0%
27Pyston #6 2.212.2127,340498  2% 98% 0% 0%
29Pyston #6 2.332.3327,464498  0% 100% 0% 0%
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%
37Nuitka #8 3.023.029,948594  2% 6% 1% 100%
38Nuitka #6 3.053.0510,088498  4% 8% 100% 1%
38Nuitka #6 3.053.069,892498  100% 9% 6% 2%
38Nuitka #8 3.063.079,684594  1% 8% 100% 2%
38Nuitka #8 3.073.089,836594  4% 8% 1% 100%
38Nuitka #6 3.063.109,916498  3% 31% 4% 75%
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%
40Python development version #6 3.213.228,616498  2% 1% 100% 1%
40Python development version #6 3.223.238,676498  0% 19% 2% 82%
40Python 3 #6 3.273.278,848498  7% 2% 4% 99%
41Python development version #6 3.333.338,716498  0% 1% 100% 1%
41Python 3 #6 3.343.348,696498  5% 2% 100% 1%
41Python 3 #6 3.293.368,664498  44% 1% 59% 1%
49Python development version #8 3.933.938,836594  1% 100% 1% 1%
49Python development version #8 3.933.948,784594  1% 0% 100% 1%
49Python development version #8 3.963.968,828594  99% 3% 3% 2%
51Python 3 #8 4.114.118,876594  7% 100% 2% 3%
51Python 3 #8 4.134.148,824594  7% 3% 100% 3%
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 4.114.208,804594  47% 1% 58% 2%
56Graal #6 8.804.50667,236498  25% 65% 62% 75%
57IronPython #6 4.344.5974,580498  1% 2% 13% 79%
57Graal #6 8.844.59670,628498  60% 66% 25% 62%
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%
59Graal #6 9.654.82682,976498  21% 68% 75% 59%
60Jython #8 8.374.87297,264594  51% 45% 38% 38%
61Jython #8 8.414.96294,976594  31% 64% 46% 29%
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%
88MicroPython #6 7.127.154,424498  2% 100% 2% 3%
90MicroPython #6 7.267.294,156498  38% 100% 9% 7%
92MicroPython #6 7.437.474,452498  9% 78% 29% 13%
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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