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%
1.7Python 3 #2 0.130.14?394  100% 0% 0% 8%
1.7Python 2 #2 0.090.14?394  7% 29% 67% 7%
1.8PyPy 2 #8 0.140.14?594  7% 93% 0% 7%
1.8PyPy 2 #8 0.150.15?594  7% 7% 93% 7%
1.9PyPy 3 #8 0.160.16?594  7% 0% 100% 6%
2.0PyPy 3 #8 0.160.16?594  7% 25% 94% 13%
2.1PyPy 2 #8 0.150.17?594  12% 0% 0% 88%
2.1PyPy 3 #8 0.150.17?594  11% 0% 6% 82%
3.0PyPy 2 #6 0.240.241,264498  9% 100% 8% 4%
3.1PyPy 2 #6 0.250.251,300498  8% 100% 16% 12%
3.3PyPy 2 #6 0.250.271,296498  15% 7% 86% 11%
4.1PyPy 3 #6 0.330.341,024498  22% 100% 12% 17%
4.3PyPy 3 #6 0.350.351,120498  15% 100% 30% 22%
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%
4.7PyPy 3 #6 0.380.381,092498  38% 89% 100% 30%
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%
10PyPy 2 #5 1.380.82252,348595  34% 64% 42% 49%
10PyPy 2 #5 1.380.83310,692595  48% 41% 58% 48%
10PyPy 2 #5 1.410.84315,164595  44% 58% 52% 59%
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%
17Graal #8 1.721.37497,384594  13% 88% 47% 18%
17Graal #8 1.691.38498,760594  74% 78% 63% 24%
17PyPy 3 #5 2.381.3887,596575  44% 41% 41% 76%
18Pyston #5 4.751.43158,316595  86% 81% 85% 82%
18PyPy 3 #5 2.481.4387,880575  38% 44% 42% 70%
18Pyston #5 4.791.49158,188595  88% 79% 76% 79%
19Pyston #5 4.821.50158,392595  88% 79% 76% 77%
19PyPy 3 #5 2.511.5387,988575  40% 43% 68% 42%
21Pyston #8 1.691.6927,440594  100% 1% 0% 0%
21Pyston #8 1.691.6927,568594  0% 100% 0% 0%
21Graal #8 2.131.70499,496594  60% 73% 65% 66%
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%
54Graal #6 8.164.33660,124498  38% 60% 56% 50%
56Graal #6 8.494.53694,412498  28% 45% 69% 72%
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%
57Graal #6 8.794.65693,972498  53% 61% 59% 68%
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%
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%
87MicroPython #6 7.007.014,396498  0% 100% 1% 0%
87MicroPython #6 7.007.014,216498  100% 0% 0% 0%
87MicroPython #6 7.027.034,364498  0% 1% 0% 100%
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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