spectral-norm benchmark N=550

Each chart bar shows how many times more Code, one ↓ spectral-norm program used, compared to the program that used least Code.

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.

    sortsortsort 
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0Nuitka #2 0.230.4816,608394  100% 100% 100% 100%
1.0Nuitka #2 0.260.6214,984394  100% 100% 100% 100%
1.0Pyston #2 0.420.4257,532394  0% 10% 83% 12%
1.0Pyston #2 0.420.4255,652394  2% 0% 0% 100%
1.0Nuitka #2 0.230.5615,008394  100% 100% 100% 100%
1.0Python 3 #2 0.140.15?394  13% 100% 0% 0%
1.0Python 3 #2 0.150.15?394  7% 0% 100% 0%
1.0Pyston #2 0.420.4256,036394  0% 0% 0% 100%
1.0Python 2 #2 0.080.08?394  0% 13% 0% 89%
1.0Python 2 #2 0.080.08?394  0% 0% 11% 100%
1.0Python 3 #2 0.150.15?394  0% 0% 100% 0%
1.0Python 2 #2 0.090.14?394  7% 29% 67% 7%
1.3Python 2 #6 2.802.806,664498  2% 2% 100% 2%
1.3IronPython #6 4.454.6271,112498  46% 1% 48% 1%
1.3MicroPython #6 7.297.314,504498  100% 9% 3% 3%
1.3MicroPython #6 7.207.304,472498  58% 34% 4% 16%
1.3Python 2 #6 2.812.826,724498  2% 1% 100% 1%
1.3Pyston #6 2.212.2127,340498  2% 98% 0% 0%
1.3Pyston #6 2.202.2127,500498  0% 0% 100% 0%
1.3Jython #6 9.576.83284,740498  43% 37% 27% 33%
1.3Jython #6 9.966.90294,640498  31% 36% 56% 21%
1.3MicroPython #6 7.237.254,472498  47% 8% 56% 6%
1.3Graal #6 38.3120.54534,956498  54% 26% 43% 72%
1.3Graal #6 34.1418.74534,676498  56% 55% 67% 20%
1.3PyPy 2 #6 0.250.251,088498  12% 100% 0% 0%
1.3Python 2 #6 2.802.806,720498  1% 3% 1% 100%
1.3Jython #6 9.826.89290,672498  30% 38% 31% 44%
1.3Graal #6 36.5519.59549,552498  54% 14% 52% 80%
1.3Pyston #6 2.332.3327,464498  0% 100% 0% 0%
1.3IronPython #6 4.344.5974,580498  1% 2% 13% 79%
1.3Python development version #6 3.543.548,012498  5% 11% 1% 90%
1.3IronPython #6 4.354.6170,740498  7% 57% 1% 29%
1.3Python development version #6 3.543.698,064498  95% 1% 6% 1%
1.3PyPy 3 #6 0.310.32996498  6% 94% 3% 0%
1.3PyPy 2 #6 0.260.261,092498  7% 100% 4% 8%
1.3PyPy 3 #6 0.310.31976498  9% 100% 0% 0%
1.3Nuitka #6 3.013.0110,532498  8% 100% 8% 1%
1.3Nuitka #6 3.033.0310,116498  6% 100% 3% 0%
1.3Python 3 #6 3.713.729,080498  6% 99% 1% 1%
1.3Nuitka #6 3.023.0310,316498  5% 100% 4% 0%
1.3Python 3 #6 3.683.859,140498  100% 0% 0% 1%
1.3Python 3 #6 3.713.799,140498  46% 0% 57% 1%
1.3Python development version #6 3.603.607,876498  5% 1% 100% 1%
1.3PyPy 3 #6 0.310.311,084498  6% 0% 0% 100%
1.3PyPy 2 #6 0.250.25976498  4% 100% 4% 0%
1.3RustPython #6 152.97153.6319,056498  17% 18% 71% 19%
1.5PyPy 3 #5 2.421.4185,988575  38% 40% 64% 37%
1.5Python development version #5 8.712.4648,168575  93% 90% 93% 89%
1.5Python development version #5 8.642.4548,248575  92% 92% 91% 90%
1.5PyPy 3 #5 2.371.2587,500575  46% 68% 38% 46%
1.5Python development version #5 8.802.4848,304575  92% 92% 89% 92%
1.5PyPy 3 #5 2.431.2785,968575  44% 42% 74% 38%
1.5PyPy 3 #8 0.200.211,072594  5% 0% 5% 95%
1.5IronPython #8 4.004.1957,200594  0% 92% 1% 1%
1.5PyPy 3 #8 0.200.211,092594  0% 5% 5% 100%
1.5IronPython #8 3.984.1657,520594  0% 63% 8% 24%
1.5IronPython #8 3.994.1861,472594  92% 0% 1% 1%
1.5Python 3 #8 4.714.718,840594  5% 100% 1% 0%
1.5Jython #8 8.414.96294,976594  31% 64% 46% 29%
1.5Python development version #8 4.254.267,988594  6% 1% 1% 100%
1.5Jython #8 8.374.87297,264594  51% 45% 38% 38%
1.5Python development version #8 4.234.247,928594  5% 100% 1% 1%
1.5Python development version #8 4.284.297,820594  5% 0% 100% 1%
1.5PyPy 2 #8 0.200.20?594  5% 100% 0% 5%
1.5PyPy 2 #8 0.200.211,088594  100% 0% 0% 0%
1.5PyPy 2 #8 0.200.23980594  90% 13% 0% 0%
1.5PyPy 3 #8 0.250.27984594  11% 11% 85% 0%
1.5Jython #8 8.084.77294,356594  43% 34% 60% 31%
1.5Graal #8 20.4310.93524,288594  48% 72% 54% 31%
1.5Graal #8 18.339.78553,204594  61% 67% 63% 4%
1.5Python 3 #8 4.624.629,036594  5% 100% 0% 1%
1.5Python 3 #8 4.624.838,884594  100% 0% 0% 0%
1.5Graal #8 22.7413.69519,948594  69% 84% 86% 75%
1.5Pyston #8 1.691.6927,440594  100% 1% 0% 0%
1.5Pyston #8 1.731.7327,432594  1% 0% 2% 98%
1.5Python 2 #8 3.123.126,864594  1% 2% 100% 1%
1.5Python 2 #8 3.163.166,796594  2% 1% 100% 1%
1.5Nuitka #8 3.053.1410,192594  69% 32% 2% 0%
1.5Python 2 #8 3.153.156,932594  2% 3% 1% 100%
1.5Nuitka #8 3.013.1510,168594  100% 7% 1% 1%
1.5Nuitka #8 2.993.1310,180594  100% 4% 0% 1%
1.5Pyston #8 1.691.6927,568594  0% 100% 0% 0%
1.5Pyston #5 4.821.50158,392595  88% 79% 76% 77%
1.5PyPy 2 #5 1.570.8988,324595  40% 63% 36% 47%
1.5PyPy 2 #5 1.530.8688,444595  36% 41% 63% 42%
1.5Python 2 #5 6.341.9541,648595  92% 92% 91% 93%
1.5Pyston #5 4.751.43158,316595  86% 81% 85% 82%
1.5Pyston #5 4.791.49158,188595  88% 79% 76% 79%
1.5PyPy 2 #5 1.510.8588,188595  46% 42% 63% 37%
1.5Python 2 #5 6.381.8441,668595  90% 93% 91% 92%
1.5Python 2 #5 6.451.8941,868595  95% 91% 92% 93%
1.6Nuitka #3 0.460.4637,412642  4% 2% 100% 0%
1.6Nuitka #3 0.450.4727,620642  98% 0% 0% 4%
1.6Python 3 #3 0.460.4631,100642  4% 2% 100% 2%
1.6Nuitka #3 0.460.4635,584642  4% 0% 100% 0%
1.6Python 3 #3 0.450.4635,516642  4% 4% 100% 0%
1.6Python 3 #3 0.460.4635,596642  7% 0% 100% 0%
1.7Numba 0.920.9284,664663  0% 3% 49% 48%
1.7Numba 0.910.9184,284663  0% 0% 0% 100%
1.7Numba 0.930.9383,916663  1% 0% 0% 99%
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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