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% 0% 11% 100%
1.0Python 2 #2 0.080.08?394  0% 13% 0% 89%
1.5Python 3 #2 0.120.12?394  8% 100% 8% 0%
1.5Python 3 #2 0.120.12?394  14% 100% 15% 0%
1.6Nuitka #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 3 #2 0.130.14?394  100% 0% 0% 8%
1.7Python 2 #2 0.090.14?394  7% 29% 67% 7%
2.4PyPy 2 #8 0.190.20?594  0% 5% 100% 0%
2.4PyPy 2 #8 0.200.20?594  0% 5% 0% 100%
2.5PyPy 3 #8 0.200.20?594  0% 5% 100% 5%
2.5PyPy 3 #8 0.200.20?594  100% 0% 5% 10%
2.6PyPy 3 #8 0.200.211,036594  62% 48% 29% 10%
2.9PyPy 2 #6 0.230.231,420498  100% 14% 4% 4%
2.9PyPy 2 #6 0.230.231,408498  0% 9% 4% 100%
3.0PyPy 2 #8 0.240.241,412594  100% 12% 0% 4%
3.1PyPy 2 #6 0.230.251,412498  4% 92% 4% 0%
3.7PyPy 3 #6 0.300.301,116498  0% 3% 0% 97%
3.8PyPy 3 #6 0.300.311,136498  100% 6% 3% 3%
4.5PyPy 3 #6 0.340.361,116498  3% 97% 5% 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%
5.1Python 3 #3 0.390.4131,192642  100% 5% 5% 2%
5.2Nuitka #3 0.420.4235,928642  100% 0% 2% 0%
5.2Pyston #2 0.420.4256,036394  0% 0% 0% 100%
5.2Nuitka #3 0.420.4230,432642  5% 2% 100% 5%
5.2Pyston #2 0.420.4255,652394  2% 0% 0% 100%
5.2Pyston #2 0.420.4257,532394  0% 10% 83% 12%
5.3Nuitka #3 0.430.4327,104642  100% 0% 0% 2%
7.9Graal #8 0.850.64299,944594  11% 10% 19% 98%
8.0Graal #8 0.850.65299,240594  97% 19% 11% 16%
8.0Graal #8 0.850.65298,652594  11% 16% 98% 12%
10PyPy 2 #5 1.430.82305,776595  46% 41% 63% 34%
10PyPy 2 #5 1.450.83300,748595  43% 41% 36% 66%
11PyPy 2 #5 1.440.8787,344595  40% 58% 36% 41%
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%
17PyPy 3 #5 2.391.3787,412575  38% 41% 39% 68%
17PyPy 3 #5 2.391.4086,904575  40% 38% 36% 65%
17PyPy 3 #5 2.391.4187,660575  35% 67% 36% 40%
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%
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%
25Python 3 #5 7.592.0652,984575  95% 95% 94% 97%
25Python 3 #5 7.632.0653,464575  94% 98% 93% 96%
27Python 3 #5 7.582.1753,204575  90% 92% 91% 90%
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%
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%
40Python 3 #6 3.273.278,848498  7% 2% 4% 99%
41Python 3 #6 3.343.348,696498  5% 2% 100% 1%
41Python 3 #6 3.293.368,664498  44% 1% 59% 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%
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%
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%
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%
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%
63Graal #6 9.805.14596,620498  56% 85% 53% 5%
70Graal #6 11.365.66623,692498  62% 68% 14% 64%
73Graal #6 11.765.88628,644498  6% 66% 80% 55%
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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