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
1.0Python 3 #2 0.170.17?394  100% 12% 6% 0%
1.0Pyston #2 0.420.4256,036394  0% 0% 0% 100%
1.0Python 2 #2 0.080.08?394  0% 0% 11% 100%
1.0Pyston #2 0.420.4255,652394  2% 0% 0% 100%
1.0Nuitka #2 0.130.13?394  0% 100% 0% 0%
1.0Pyston #2 0.420.4257,532394  0% 10% 83% 12%
1.0Python 3 #2 0.170.17?394  94% 6% 6% 0%
1.0Nuitka #2 0.130.13?394  0% 0% 100% 0%
1.0Python 2 #2 0.090.14?394  7% 29% 67% 7%
1.0Nuitka #2 0.130.13?394  0% 0% 100% 0%
1.0Python 2 #2 0.080.08?394  0% 13% 0% 89%
1.0Python 3 #2 0.180.18?394  95% 17% 21% 6%
1.3Jython #6 9.966.90294,640498  31% 36% 56% 21%
1.3Python 2 #6 2.802.806,720498  1% 3% 1% 100%
1.3Jython #6 9.576.83284,740498  43% 37% 27% 33%
1.3Python 2 #6 2.802.806,664498  2% 2% 100% 2%
1.3Jython #6 9.826.89290,672498  30% 38% 31% 44%
1.3Python 2 #6 2.812.826,724498  2% 1% 100% 1%
1.3Graal #6 10.435.43594,008498  42% 67% 50% 45%
1.3Pyston #6 2.202.2127,500498  0% 0% 100% 0%
1.3IronPython #6 4.354.6170,740498  7% 57% 1% 29%
1.3Pyston #6 2.212.2127,340498  2% 98% 0% 0%
1.3IronPython #6 4.344.5974,580498  1% 2% 13% 79%
1.3RustPython #6 152.97153.6319,056498  17% 18% 71% 19%
1.3IronPython #6 4.454.6271,112498  46% 1% 48% 1%
1.3Pyston #6 2.332.3327,464498  0% 100% 0% 0%
1.3Python 3 #6 3.453.458,752498  3% 9% 100% 2%
1.3MicroPython #6 7.297.314,504498  100% 9% 3% 3%
1.3Nuitka #6 2.952.9510,344498  1% 100% 1% 2%
1.3Python development version #6 3.543.548,012498  5% 11% 1% 90%
1.3Python development version #6 3.603.607,876498  5% 1% 100% 1%
1.3Python development version #6 3.543.698,064498  95% 1% 6% 1%
1.3MicroPython #6 7.207.304,472498  58% 34% 4% 16%
1.3PyPy 2 #6 0.250.261,124498  35% 73% 11% 14%
1.3PyPy 3 #6 0.310.341,140498  24% 73% 6% 3%
1.3Python 3 #6 3.423.588,752498  4% 100% 2% 1%
1.3PyPy 2 #6 0.240.241,116498  63% 8% 43% 12%
1.3MicroPython #6 7.237.254,472498  47% 8% 56% 6%
1.3Python 3 #6 3.463.618,896498  6% 100% 1% 1%
1.3Graal #6 10.435.33598,512498  39% 69% 52% 47%
1.3Graal #6 10.055.37590,104498  46% 57% 89% 5%
1.3PyPy 2 #6 0.230.2421,304498  0% 100% 20% 4%
1.3PyPy 3 #6 0.310.321,116498  6% 100% 13% 3%
1.3PyPy 3 #6 0.310.311,124498  0% 3% 100% 0%
1.3Nuitka #6 2.942.9410,344498  3% 100% 2% 1%
1.3Nuitka #6 2.962.9610,220498  1% 75% 2% 27%
1.5Nuitka #5 6.621.9257,920575  87% 86% 87% 92%
1.5Python 3 #5 7.902.2854,080575  91% 93% 91% 90%
1.5Python 3 #5 7.952.3754,024575  93% 93% 92% 90%
1.5Nuitka #5 6.581.8458,556575  90% 93% 90% 91%
1.5Python 3 #5 7.982.2453,856575  92% 92% 92% 96%
1.5Python development version #5 8.712.4648,168575  93% 90% 93% 89%
1.5PyPy 3 #5 2.421.3785,080575  38% 48% 68% 40%
1.5Python development version #5 8.802.4848,304575  92% 92% 89% 92%
1.5Python development version #5 8.642.4548,248575  92% 92% 91% 90%
1.5Nuitka #5 6.501.7857,680575  93% 91% 92% 93%
1.5PyPy 3 #5 2.481.4885,576575  38% 67% 37% 41%
1.5PyPy 3 #5 2.451.4186,212575  40% 45% 37% 66%
1.5Pyston #8 1.691.6927,440594  100% 1% 0% 0%
1.5PyPy 3 #8 0.200.211,120594  9% 95% 0% 0%
1.5PyPy 3 #8 0.200.20?594  100% 5% 0% 0%
1.5Python 3 #8 4.194.328,728594  3% 76% 4% 28%
1.5Python 2 #8 3.123.126,864594  1% 2% 100% 1%
1.5IronPython #8 3.994.1861,472594  92% 0% 1% 1%
1.5Nuitka #8 2.972.989,856594  86% 2% 14% 2%
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%
1.5IronPython #8 4.004.1957,200594  0% 92% 1% 1%
1.5Python development version #8 4.254.267,988594  6% 1% 1% 100%
1.5Python 3 #8 4.194.208,908594  92% 12% 3% 10%
1.5Jython #8 8.374.87297,264594  51% 45% 38% 38%
1.5Pyston #8 1.731.7327,432594  1% 0% 2% 98%
1.5Jython #8 8.084.77294,356594  43% 34% 60% 31%
1.5PyPy 2 #8 0.200.20?594  37% 10% 5% 64%
1.5Jython #8 8.414.96294,976594  31% 64% 46% 29%
1.5IronPython #8 3.984.1657,520594  0% 63% 8% 24%
1.5Nuitka #8 2.952.959,872594  100% 1% 0% 1%
1.5PyPy 2 #8 0.210.211,112594  100% 73% 33% 15%
1.5Nuitka #8 2.912.9110,004594  2% 100% 2% 2%
1.5PyPy 2 #8 0.200.20?594  100% 19% 10% 5%
1.5Python 3 #8 4.334.348,764594  14% 13% 91% 6%
1.5Pyston #8 1.691.6927,568594  0% 100% 0% 0%
1.5Python 2 #8 3.153.156,932594  2% 3% 1% 100%
1.5PyPy 3 #8 0.200.211,140594  5% 100% 5% 0%
1.5Graal #8 1.020.79297,936594  28% 1% 15% 97%
1.5Graal #8 0.860.65298,888594  21% 71% 17% 38%
1.5Python 2 #8 3.163.166,796594  2% 1% 100% 1%
1.5Graal #8 1.070.83406,280594  12% 51% 65% 12%
1.5PyPy 2 #5 1.661.0188,640595  60% 68% 66% 71%
1.5Python 2 #5 6.341.9541,648595  92% 92% 91% 93%
1.5Pyston #5 4.791.49158,188595  88% 79% 76% 79%
1.5Python 2 #5 6.381.8441,668595  90% 93% 91% 92%
1.5PyPy 2 #5 1.570.9188,928595  76% 55% 51% 47%
1.5Pyston #5 4.821.50158,392595  88% 79% 76% 77%
1.5PyPy 2 #5 1.470.84314,504595  47% 49% 67% 45%
1.5Pyston #5 4.751.43158,316595  86% 81% 85% 82%
1.5Python 2 #5 6.451.8941,868595  95% 91% 92% 93%
1.6Python 3 #3 0.490.4939,228642  98% 4% 6% 0%
1.6Python 3 #3 0.470.4741,308642  4% 4% 9% 100%
1.6Python 3 #3 0.470.4739,464642  2% 8% 4% 100%
1.6Nuitka #3 0.420.4235,928642  100% 0% 2% 0%
1.6Nuitka #3 0.430.4327,104642  100% 0% 0% 2%
1.6Nuitka #3 0.420.4230,432642  5% 2% 100% 5%
1.7Numba 0.920.9284,664663  0% 3% 49% 48%
1.7Numba 0.930.9383,916663  1% 0% 0% 99%
1.7Numba 0.910.9184,284663  0% 0% 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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