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%
1.6Nuitka #2 0.130.13?394  0% 0% 100% 0%
1.6Nuitka #2 0.130.13?394  0% 100% 0% 0%
1.6Nuitka #2 0.130.13?394  0% 0% 100% 0%
1.7Python 2 #2 0.090.14?394  7% 29% 67% 7%
2.4PyPy 2 #8 0.200.20?594  100% 19% 10% 5%
2.5PyPy 2 #8 0.200.20?594  37% 10% 5% 64%
2.5Python 3 #2 0.200.20?394  5% 0% 0% 100%
2.5PyPy 3 #8 0.200.20?594  100% 5% 0% 0%
2.6Python 3 #2 0.200.211,792394  100% 5% 5% 10%
2.6Python 3 #2 0.200.211,936394  13% 9% 100% 0%
2.6PyPy 3 #8 0.200.211,140594  5% 100% 5% 0%
2.6PyPy 3 #8 0.200.211,120594  9% 95% 0% 0%
2.6PyPy 2 #8 0.210.211,112594  100% 73% 33% 15%
3.0PyPy 2 #6 0.230.2421,304498  0% 100% 20% 4%
3.0PyPy 2 #6 0.240.241,116498  63% 8% 43% 12%
3.2PyPy 2 #6 0.250.261,124498  35% 73% 11% 14%
3.8PyPy 3 #6 0.310.311,124498  0% 3% 100% 0%
4.0PyPy 3 #6 0.310.321,116498  6% 100% 13% 3%
4.2PyPy 3 #6 0.310.341,140498  24% 73% 6% 3%
5.2Nuitka #3 0.420.4235,928642  100% 0% 2% 0%
5.2Pyston #2 0.420.4256,036394  0% 0% 0% 100%
5.2Pyston #2 0.420.4255,652394  2% 0% 0% 100%
5.2Nuitka #3 0.420.4230,432642  5% 2% 100% 5%
5.2Pyston #2 0.420.4257,532394  0% 10% 83% 12%
5.3Nuitka #3 0.430.4327,104642  100% 0% 0% 2%
6.2Python 3 #3 0.500.5033,008642  10% 84% 6% 18%
6.3Python 3 #3 0.510.5133,708642  8% 4% 100% 4%
6.7Python 3 #3 0.530.5443,224642  39% 4% 70% 6%
9.6Graal #8 1.010.78276,908594  29% 18% 16% 97%
9.8Graal #8 1.040.79272,656594  23% 38% 94% 22%
10PyPy 2 #5 1.470.84314,504595  47% 49% 67% 45%
11Numba 0.910.9184,284663  0% 0% 0% 100%
11PyPy 2 #5 1.570.9188,928595  76% 55% 51% 47%
11Numba 0.920.9284,664663  0% 3% 49% 48%
11Numba 0.930.9383,916663  1% 0% 0% 99%
12Graal #8 1.120.93386,712594  100% 39% 40% 52%
13PyPy 2 #5 1.661.0188,640595  60% 68% 66% 71%
17PyPy 3 #5 2.421.3785,080575  38% 48% 68% 40%
17PyPy 3 #5 2.451.4186,212575  40% 45% 37% 66%
18Pyston #5 4.751.43158,316595  86% 81% 85% 82%
18PyPy 3 #5 2.481.4885,576575  38% 67% 37% 41%
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%
22Nuitka #5 6.501.7857,680575  93% 91% 92% 93%
23Python 2 #5 6.381.8441,668595  90% 93% 91% 92%
23Nuitka #5 6.581.8458,556575  90% 93% 90% 91%
23Python 2 #5 6.451.8941,868595  95% 91% 92% 93%
24Nuitka #5 6.621.9257,920575  87% 86% 87% 92%
24Python 2 #5 6.341.9541,648595  92% 92% 91% 93%
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%
30Python development version #5 8.642.4548,248575  92% 92% 91% 90%
30Python development version #5 8.712.4648,168575  93% 90% 93% 89%
31Python development version #5 8.802.4848,304575  92% 92% 89% 92%
31Python 3 #5 8.912.5547,752575  92% 90% 89% 92%
33Python 3 #5 9.032.7048,704575  94% 92% 92% 91%
35Python 2 #6 2.802.806,664498  2% 2% 100% 2%
35Python 2 #6 2.802.806,720498  1% 3% 1% 100%
35Python 2 #6 2.812.826,724498  2% 1% 100% 1%
36Nuitka #8 2.912.9110,004594  2% 100% 2% 2%
36Nuitka #6 2.942.9410,344498  3% 100% 2% 1%
36Nuitka #8 2.952.959,872594  100% 1% 0% 1%
36Nuitka #6 2.952.9510,344498  1% 100% 1% 2%
37Nuitka #6 2.962.9610,220498  1% 75% 2% 27%
37Nuitka #8 2.972.989,856594  86% 2% 14% 2%
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%
44Python development version #6 3.543.548,012498  5% 11% 1% 90%
44Python development version #6 3.603.607,876498  5% 1% 100% 1%
46Python development version #6 3.543.698,064498  95% 1% 6% 1%
49Python 3 #6 3.973.979,140498  13% 8% 100% 7%
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 #6 4.204.229,128498  17% 13% 100% 12%
52Python development version #8 4.234.247,928594  5% 100% 1% 1%
53Python development version #8 4.254.267,988594  6% 1% 1% 100%
53Python development version #8 4.284.297,820594  5% 0% 100% 1%
54Python 3 #6 4.374.389,172498  22% 15% 100% 14%
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%
57Python 3 #8 4.624.628,908594  7% 3% 2% 100%
57Python 3 #8 4.634.648,924594  7% 39% 8% 60%
57Graal #6 8.474.66557,296498  42% 87% 50% 18%
59Graal #6 8.684.76611,672498  53% 54% 83% 24%
59Graal #6 8.764.77566,296498  54% 57% 41% 47%
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%
64Python 3 #8 5.045.168,816594  39% 18% 31% 64%
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%
89MicroPython #6 7.237.254,472498  47% 8% 56% 6%
90MicroPython #6 7.207.304,472498  58% 34% 4% 16%
90MicroPython #6 7.297.314,504498  100% 9% 3% 3%
1,897RustPython #6 152.97153.6319,056498  17% 18% 71% 19%
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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