x64 ArchLinux : Intel® i5-7200U® 
Python Interpreters Benchmarks      [[ Play ]]

- all benchmarks - n-body fannkuch-redux meteor-contest fasta fasta-redux fibonacci fib50 spectral-norm reverse-complement mandelbrot k-nucleotide regex-dna pidigits chameneos-redux templates thread-ring binary-trees-redux binary-trees pystone richards iobench jsonbench - all languages - Python 2 PyPy Python 3 Python dev PyPy 3 Jython IronPython Cython Nuitka Shedskin Numba Pyston MicroPython Grumpy Graal RustPython x64 dual-core

 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.

		sort	sort	sort
×	Program Source Code	CPU secs	Elapsed secs	Memory KB	Code B	≈ CPU Load
1.0	Python 3 #2	0.17	0.17	?	394	100% 12% 6% 0%
1.0	Pyston #2	0.42	0.42	56,036	394	0% 0% 0% 100%
1.0	Python 2 #2	0.08	0.08	?	394	0% 0% 11% 100%
1.0	Pyston #2	0.42	0.42	55,652	394	2% 0% 0% 100%
1.0	Nuitka #2	0.13	0.13	?	394	0% 100% 0% 0%
1.0	Pyston #2	0.42	0.42	57,532	394	0% 10% 83% 12%
1.0	Python 3 #2	0.17	0.17	?	394	94% 6% 6% 0%
1.0	Nuitka #2	0.13	0.13	?	394	0% 0% 100% 0%
1.0	Python 2 #2	0.09	0.14	?	394	7% 29% 67% 7%
1.0	Nuitka #2	0.13	0.13	?	394	0% 0% 100% 0%
1.0	Python 2 #2	0.08	0.08	?	394	0% 13% 0% 89%
1.0	Python 3 #2	0.18	0.18	?	394	95% 17% 21% 6%
1.3	Jython #6	9.96	6.90	294,640	498	31% 36% 56% 21%
1.3	Python 2 #6	2.80	2.80	6,720	498	1% 3% 1% 100%
1.3	Jython #6	9.57	6.83	284,740	498	43% 37% 27% 33%
1.3	Python 2 #6	2.80	2.80	6,664	498	2% 2% 100% 2%
1.3	Jython #6	9.82	6.89	290,672	498	30% 38% 31% 44%
1.3	Python 2 #6	2.81	2.82	6,724	498	2% 1% 100% 1%
1.3	Graal #6	10.43	5.43	594,008	498	42% 67% 50% 45%
1.3	Pyston #6	2.20	2.21	27,500	498	0% 0% 100% 0%
1.3	IronPython #6	4.35	4.61	70,740	498	7% 57% 1% 29%
1.3	Pyston #6	2.21	2.21	27,340	498	2% 98% 0% 0%
1.3	IronPython #6	4.34	4.59	74,580	498	1% 2% 13% 79%
1.3	RustPython #6	152.97	153.63	19,056	498	17% 18% 71% 19%
1.3	IronPython #6	4.45	4.62	71,112	498	46% 1% 48% 1%
1.3	Pyston #6	2.33	2.33	27,464	498	0% 100% 0% 0%
1.3	Python 3 #6	3.45	3.45	8,752	498	3% 9% 100% 2%
1.3	MicroPython #6	7.29	7.31	4,504	498	100% 9% 3% 3%
1.3	Nuitka #6	2.95	2.95	10,344	498	1% 100% 1% 2%
1.3	Python development version #6	3.54	3.54	8,012	498	5% 11% 1% 90%
1.3	Python development version #6	3.60	3.60	7,876	498	5% 1% 100% 1%
1.3	Python development version #6	3.54	3.69	8,064	498	95% 1% 6% 1%
1.3	MicroPython #6	7.20	7.30	4,472	498	58% 34% 4% 16%
1.3	PyPy 2 #6	0.25	0.26	1,124	498	35% 73% 11% 14%
1.3	PyPy 3 #6	0.31	0.34	1,140	498	24% 73% 6% 3%
1.3	Python 3 #6	3.42	3.58	8,752	498	4% 100% 2% 1%
1.3	PyPy 2 #6	0.24	0.24	1,116	498	63% 8% 43% 12%
1.3	MicroPython #6	7.23	7.25	4,472	498	47% 8% 56% 6%
1.3	Python 3 #6	3.46	3.61	8,896	498	6% 100% 1% 1%
1.3	Graal #6	10.43	5.33	598,512	498	39% 69% 52% 47%
1.3	Graal #6	10.05	5.37	590,104	498	46% 57% 89% 5%
1.3	PyPy 2 #6	0.23	0.24	21,304	498	0% 100% 20% 4%
1.3	PyPy 3 #6	0.31	0.32	1,116	498	6% 100% 13% 3%
1.3	PyPy 3 #6	0.31	0.31	1,124	498	0% 3% 100% 0%
1.3	Nuitka #6	2.94	2.94	10,344	498	3% 100% 2% 1%
1.3	Nuitka #6	2.96	2.96	10,220	498	1% 75% 2% 27%
1.5	Nuitka #5	6.62	1.92	57,920	575	87% 86% 87% 92%
1.5	Python 3 #5	7.90	2.28	54,080	575	91% 93% 91% 90%
1.5	Python 3 #5	7.95	2.37	54,024	575	93% 93% 92% 90%
1.5	Nuitka #5	6.58	1.84	58,556	575	90% 93% 90% 91%
1.5	Python 3 #5	7.98	2.24	53,856	575	92% 92% 92% 96%
1.5	Python development version #5	8.71	2.46	48,168	575	93% 90% 93% 89%
1.5	PyPy 3 #5	2.42	1.37	85,080	575	38% 48% 68% 40%
1.5	Python development version #5	8.80	2.48	48,304	575	92% 92% 89% 92%
1.5	Python development version #5	8.64	2.45	48,248	575	92% 92% 91% 90%
1.5	Nuitka #5	6.50	1.78	57,680	575	93% 91% 92% 93%
1.5	PyPy 3 #5	2.48	1.48	85,576	575	38% 67% 37% 41%
1.5	PyPy 3 #5	2.45	1.41	86,212	575	40% 45% 37% 66%
1.5	Pyston #8	1.69	1.69	27,440	594	100% 1% 0% 0%
1.5	PyPy 3 #8	0.20	0.21	1,120	594	9% 95% 0% 0%
1.5	PyPy 3 #8	0.20	0.20	?	594	100% 5% 0% 0%
1.5	Python 3 #8	4.19	4.32	8,728	594	3% 76% 4% 28%
1.5	Python 2 #8	3.12	3.12	6,864	594	1% 2% 100% 1%
1.5	IronPython #8	3.99	4.18	61,472	594	92% 0% 1% 1%
1.5	Nuitka #8	2.97	2.98	9,856	594	86% 2% 14% 2%
1.5	Python development version #8	4.23	4.24	7,928	594	5% 100% 1% 1%
1.5	Python development version #8	4.28	4.29	7,820	594	5% 0% 100% 1%
1.5	IronPython #8	4.00	4.19	57,200	594	0% 92% 1% 1%
1.5	Python development version #8	4.25	4.26	7,988	594	6% 1% 1% 100%
1.5	Python 3 #8	4.19	4.20	8,908	594	92% 12% 3% 10%
1.5	Jython #8	8.37	4.87	297,264	594	51% 45% 38% 38%
1.5	Pyston #8	1.73	1.73	27,432	594	1% 0% 2% 98%
1.5	Jython #8	8.08	4.77	294,356	594	43% 34% 60% 31%
1.5	PyPy 2 #8	0.20	0.20	?	594	37% 10% 5% 64%
1.5	Jython #8	8.41	4.96	294,976	594	31% 64% 46% 29%
1.5	IronPython #8	3.98	4.16	57,520	594	0% 63% 8% 24%
1.5	Nuitka #8	2.95	2.95	9,872	594	100% 1% 0% 1%
1.5	PyPy 2 #8	0.21	0.21	1,112	594	100% 73% 33% 15%
1.5	Nuitka #8	2.91	2.91	10,004	594	2% 100% 2% 2%
1.5	PyPy 2 #8	0.20	0.20	?	594	100% 19% 10% 5%
1.5	Python 3 #8	4.33	4.34	8,764	594	14% 13% 91% 6%
1.5	Pyston #8	1.69	1.69	27,568	594	0% 100% 0% 0%
1.5	Python 2 #8	3.15	3.15	6,932	594	2% 3% 1% 100%
1.5	PyPy 3 #8	0.20	0.21	1,140	594	5% 100% 5% 0%
1.5	Graal #8	1.02	0.79	297,936	594	28% 1% 15% 97%
1.5	Graal #8	0.86	0.65	298,888	594	21% 71% 17% 38%
1.5	Python 2 #8	3.16	3.16	6,796	594	2% 1% 100% 1%
1.5	Graal #8	1.07	0.83	406,280	594	12% 51% 65% 12%
1.5	PyPy 2 #5	1.66	1.01	88,640	595	60% 68% 66% 71%
1.5	Python 2 #5	6.34	1.95	41,648	595	92% 92% 91% 93%
1.5	Pyston #5	4.79	1.49	158,188	595	88% 79% 76% 79%
1.5	Python 2 #5	6.38	1.84	41,668	595	90% 93% 91% 92%
1.5	PyPy 2 #5	1.57	0.91	88,928	595	76% 55% 51% 47%
1.5	Pyston #5	4.82	1.50	158,392	595	88% 79% 76% 77%
1.5	PyPy 2 #5	1.47	0.84	314,504	595	47% 49% 67% 45%
1.5	Pyston #5	4.75	1.43	158,316	595	86% 81% 85% 82%
1.5	Python 2 #5	6.45	1.89	41,868	595	95% 91% 92% 93%
1.6	Python 3 #3	0.49	0.49	39,228	642	98% 4% 6% 0%
1.6	Python 3 #3	0.47	0.47	41,308	642	4% 4% 9% 100%
1.6	Python 3 #3	0.47	0.47	39,464	642	2% 8% 4% 100%
1.6	Nuitka #3	0.42	0.42	35,928	642	100% 0% 2% 0%
1.6	Nuitka #3	0.43	0.43	27,104	642	100% 0% 0% 2%
1.6	Nuitka #3	0.42	0.42	30,432	642	5% 2% 100% 5%
1.7	Numba	0.92	0.92	84,664	663	0% 3% 49% 48%
1.7	Numba	0.93	0.93	83,916	663	1% 0% 0% 99%
1.7	Numba	0.91	0.91	84,284	663	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.

  Home   Conclusions   License   Play