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.

     sortsortsort
  ×   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%
1.1Python 2 #2 0.090.14?394  7% 29% 67% 7%
1.8Python 3 #2 0.140.15?394  13% 100% 0% 0%
1.8Python 3 #2 0.150.15?394  7% 0% 100% 0%
1.8Python 3 #2 0.150.15?394  0% 0% 100% 0%
2.5PyPy 2 #8 0.200.211,088594  100% 0% 0% 0%
2.5PyPy 2 #8 0.200.20?594  5% 100% 0% 5%
2.5PyPy 2 #8 0.200.23980594  90% 13% 0% 0%
2.5PyPy 3 #8 0.200.211,072594  5% 0% 5% 95%
2.6PyPy 3 #8 0.200.211,092594  0% 5% 5% 100%
2.9Nuitka #2 0.230.4816,608394  100% 100% 100% 100%
2.9Nuitka #2 0.230.5615,008394  100% 100% 100% 100%
3.1PyPy 2 #6 0.250.25976498  4% 100% 4% 0%
3.1PyPy 2 #6 0.250.251,088498  12% 100% 0% 0%
3.2PyPy 3 #8 0.250.27984594  11% 11% 85% 0%
3.2PyPy 2 #6 0.260.261,092498  7% 100% 4% 8%
3.3Nuitka #2 0.260.6214,984394  100% 100% 100% 100%
3.9PyPy 3 #6 0.310.31976498  9% 100% 0% 0%
3.9PyPy 3 #6 0.310.32996498  6% 94% 3% 0%
3.9PyPy 3 #6 0.310.311,084498  6% 0% 0% 100%
5.3Pyston #2 0.420.4255,652394  2% 0% 0% 100%
5.3Pyston #2 0.420.4256,036394  0% 0% 0% 100%
5.3Pyston #2 0.420.4257,532394  0% 10% 83% 12%
5.7Nuitka #3 0.450.4727,620642  98% 0% 0% 4%
5.7Python 3 #3 0.450.4635,516642  4% 4% 100% 0%
5.8Python 3 #3 0.460.4635,596642  7% 0% 100% 0%
5.8Nuitka #3 0.460.4637,412642  4% 2% 100% 0%
5.8Python 3 #3 0.460.4631,100642  4% 2% 100% 2%
5.8Nuitka #3 0.460.4635,584642  4% 0% 100% 0%
11Numba 0.910.9184,284663  0% 0% 0% 100%
12Numba 0.920.9284,664663  0% 3% 49% 48%
12Numba 0.930.9383,916663  1% 0% 0% 99%
19PyPy 2 #5 1.510.8588,188595  46% 42% 63% 37%
19PyPy 2 #5 1.530.8688,444595  36% 41% 63% 42%
20PyPy 2 #5 1.570.8988,324595  40% 63% 36% 47%
21Pyston #8 1.691.6927,440594  100% 1% 0% 0%
21Pyston #8 1.691.6927,568594  0% 100% 0% 0%
22Pyston #8 1.731.7327,432594  1% 0% 2% 98%
28Pyston #6 2.202.2127,500498  0% 0% 100% 0%
28Pyston #6 2.212.2127,340498  2% 98% 0% 0%
29Pyston #6 2.332.3327,464498  0% 100% 0% 0%
30PyPy 3 #5 2.371.2587,500575  46% 68% 38% 46%
31PyPy 3 #5 2.421.4185,988575  38% 40% 64% 37%
31PyPy 3 #5 2.431.2785,968575  44% 42% 74% 38%
35Python 2 #6 2.802.806,664498  2% 2% 100% 2%
35Python 2 #6 2.802.806,720498  1% 3% 1% 100%
36Python 2 #6 2.812.826,724498  2% 1% 100% 1%
38Nuitka #8 2.993.1310,180594  100% 4% 0% 1%
38Nuitka #6 3.013.0110,532498  8% 100% 8% 1%
38Nuitka #8 3.013.1510,168594  100% 7% 1% 1%
38Nuitka #6 3.023.0310,316498  5% 100% 4% 0%
38Nuitka #6 3.033.0310,116498  6% 100% 3% 0%
39Nuitka #8 3.053.1410,192594  69% 32% 2% 0%
39Python 2 #8 3.123.126,864594  1% 2% 100% 1%
40Python 2 #8 3.153.156,932594  2% 3% 1% 100%
40Python 2 #8 3.163.166,796594  2% 1% 100% 1%
45Python development version #6 3.543.698,064498  95% 1% 6% 1%
45Python development version #6 3.543.548,012498  5% 11% 1% 90%
46Python development version #6 3.603.607,876498  5% 1% 100% 1%
47Python 3 #6 3.683.859,140498  100% 0% 0% 1%
47Python 3 #6 3.713.729,080498  6% 99% 1% 1%
47Python 3 #6 3.713.799,140498  46% 0% 57% 1%
50IronPython #8 3.984.1657,520594  0% 63% 8% 24%
50IronPython #8 3.994.1861,472594  92% 0% 1% 1%
51IronPython #8 4.004.1957,200594  0% 92% 1% 1%
54Python development version #8 4.234.247,928594  5% 100% 1% 1%
54Python development version #8 4.254.267,988594  6% 1% 1% 100%
54Python development version #8 4.284.297,820594  5% 0% 100% 1%
55IronPython #6 4.344.5974,580498  1% 2% 13% 79%
55IronPython #6 4.354.6170,740498  7% 57% 1% 29%
56IronPython #6 4.454.6271,112498  46% 1% 48% 1%
58Python 3 #8 4.624.629,036594  5% 100% 0% 1%
58Python 3 #8 4.624.838,884594  100% 0% 0% 0%
60Python 3 #8 4.714.718,840594  5% 100% 1% 0%
60Pyston #5 4.751.43158,316595  86% 81% 85% 82%
61Pyston #5 4.791.49158,188595  88% 79% 76% 79%
61Pyston #5 4.821.50158,392595  88% 79% 76% 77%
80Python 2 #5 6.341.9541,648595  92% 92% 91% 93%
81Python 2 #5 6.381.8441,668595  90% 93% 91% 92%
82Python 2 #5 6.451.8941,868595  95% 91% 92% 93%
91MicroPython #6 7.207.304,472498  58% 34% 4% 16%
92MicroPython #6 7.237.254,472498  47% 8% 56% 6%
92MicroPython #6 7.297.314,504498  100% 9% 3% 3%
102Jython #8 8.084.77294,356594  43% 34% 60% 31%
106Jython #8 8.374.87297,264594  51% 45% 38% 38%
106Jython #8 8.414.96294,976594  31% 64% 46% 29%
109Python development version #5 8.642.4548,248575  92% 92% 91% 90%
110Python development version #5 8.712.4648,168575  93% 90% 93% 89%
111Python development version #5 8.802.4848,304575  92% 92% 89% 92%
121Jython #6 9.576.83284,740498  43% 37% 27% 33%
124Jython #6 9.826.89290,672498  30% 38% 31% 44%
126Jython #6 9.966.90294,640498  31% 36% 56% 21%
232Graal #8 18.339.78553,204594  61% 67% 63% 4%
259Graal #8 20.4310.93524,288594  48% 72% 54% 31%
288Graal #8 22.7413.69519,948594  69% 84% 86% 75%
432Graal #6 34.1418.74534,676498  56% 55% 67% 20%
463Graal #6 36.5519.59549,552498  54% 14% 52% 80%
485Graal #6 38.3120.54534,956498  54% 26% 43% 72%
1,936RustPython #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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