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%
1.6Nuitka #2 0.130.13?394  100% 0% 0% 0%
1.6Nuitka #2 0.120.13?394  0% 0% 100% 0%
1.6Nuitka #2 0.130.13?394  0% 0% 0% 100%
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  11% 18% 0% 100%
2.5PyPy 2 #8 0.200.20?594  5% 95% 5% 0%
2.5PyPy 3 #8 0.200.20?594  0% 100% 0% 0%
2.5PyPy 3 #8 0.200.20?594  0% 0% 100% 0%
2.5PyPy 2 #8 0.200.20?594  5% 0% 5% 95%
2.7PyPy 3 #8 0.200.221,132594  0% 5% 90% 5%
2.8PyPy 2 #6 0.230.231,304498  4% 0% 100% 0%
2.9PyPy 2 #6 0.230.231,312498  12% 4% 96% 4%
2.9PyPy 2 #6 0.230.241,308498  9% 8% 100% 4%
3.7PyPy 3 #6 0.300.301,136498  0% 0% 100% 3%
3.8PyPy 3 #6 0.310.311,136498  3% 100% 0% 0%
3.8PyPy 3 #6 0.310.311,136498  6% 9% 3% 100%
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%
4.7Python 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 0.390.401,760642  100% 3% 3% 0%
4.9Nuitka #3 0.390.401,740642  100% 3% 3% 3%
5.0Nuitka #3 0.400.401,852642  0% 3% 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%
9.8PyPy 2 #5 1.390.80306,316595  60% 36% 42% 46%
10PyPy 2 #5 1.430.81305,504595  63% 42% 40% 52%
10PyPy 2 #5 1.420.83303,220595  40% 61% 34% 44%
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%
16Graal #8 1.661.29394,260594  17% 50% 38% 32%
16Graal #8 1.671.30395,540594  62% 44% 9% 24%
17Graal #8 1.701.34394,344594  61% 37% 26% 12%
17PyPy 3 #5 2.361.3786,700575  38% 62% 39% 41%
17PyPy 3 #5 2.371.3986,732575  39% 38% 41% 62%
17PyPy 3 #5 2.391.4086,752575  42% 65% 37% 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%
23Python 2 #5 6.381.8441,668595  90% 93% 91% 92%
23Nuitka #5 6.701.8757,728575  90% 90% 93% 91%
23Nuitka #5 6.781.8858,636575  92% 92% 90% 91%
23Python 2 #5 6.451.8941,868595  95% 91% 92% 93%
24Nuitka #5 6.871.9358,668575  94% 90% 88% 89%
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%
36Nuitka #8 2.912.929,856594  2% 1% 1% 100%
36Nuitka #8 2.922.929,920594  1% 0% 1% 100%
36Nuitka #8 2.922.939,752594  1% 26% 0% 74%
38Nuitka #6 3.053.059,996498  1% 2% 1% 100%
38Nuitka #6 3.053.0510,060498  1% 0% 1% 100%
38Nuitka #6 3.083.0910,092498  1% 100% 3% 3%
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%
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%
59Graal #6 9.304.76540,104498  57% 47% 76% 37%
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%
68Graal #6 10.825.54623,264498  79% 70% 24% 54%
70Graal #6 11.025.63628,028498  63% 72% 34% 67%
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 7.007.014,216498  100% 0% 0% 0%
87MicroPython #6 7.007.014,396498  0% 100% 1% 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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