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

 pidigits benchmark N=10,000

Each chart bar shows how many times more Memory, one ↓ pidigits program used, compared to the program that used least Memory.

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
	Cython #4	0.03	0.03	?	349	100% 0% 0% 0%
	Python 3 #4	0.04	0.04	?	379	100% 0% 75% 20%
	Python 3 #4	0.03	0.04	?	379	0% 40% 75% 50%
	Python 3 #4	0.04	0.05	?	379	60% 100% 80% 67%
	Nuitka #4	0.04	0.04	?	379	0% 100% 0% 0%
	Python 2 #4	0.02	0.02	?	380	50% 0% 100% 0%
	Cython #4	0.03	0.03	?	349	0% 33% 100% 0%
	Python 2 #4	0.01	0.01	?	380	0% 0% 0% 100%
	Nuitka #4	0.04	0.04	?	379	100% 20% 0% 0%
	Python 2 #4	0.01	0.01	?	380	0% 0% 100% 0%
	Nuitka #4	0.04	0.04	?	379	0% 100% 25% 0%
	Cython #4	0.03	0.03	?	349	100% 0% 0% 0%
	MicroPython	13.12	13.17	4,220	322	33% 35% 20% 84%
	MicroPython	12.86	13.03	4,248	322	10% 33% 10% 79%
	MicroPython	15.26	15.76	4,260	322	95% 82% 73% 77%
	Python 2	3.24	3.25	7,216	322	9% 100% 6% 7%
	Python 2	3.20	3.20	7,236	322	8% 3% 7% 100%
	Python 2	3.38	3.39	7,468	322	44% 14% 16% 73%
	Python development version	2.94	2.95	8,448	322	6% 2% 100% 1%
	Python development version	2.90	2.90	8,624	322	5% 64% 0% 37%
	Python development version	2.94	2.96	8,676	322	15% 2% 91% 1%
	Python 2 #2	1.04	1.04	8,820	389	2% 3% 100% 3%
	Python 2 #2	1.02	1.03	8,884	389	100% 1% 2% 1%
	Python 2 #2	1.07	1.08	8,972	389	7% 99% 8% 9%
	Python 2 #3	1.94	1.94	8,972	664	100% 3% 1% 1%
	Python 2 #3	1.94	1.94	9,020	664	100% 2% 1% 2%
	Python 2 #3	1.94	1.95	9,268	664	1% 99% 2% 2%
	Python 3	3.06	3.15	9,320	322	14% 59% 39% 25%
	Cython	2.90	2.91	9,344	322	100% 0% 1% 0%
	Python 3	3.00	3.01	9,408	322	56% 16% 52% 9%
	Python 3	3.16	3.27	9,468	322	21% 65% 59% 20%
	Cython	2.90	2.91	9,488	322	1% 100% 1% 1%
	Cython	2.90	2.90	9,500	322	100% 1% 1% 0%
	Nuitka	2.90	2.91	10,520	322	1% 100% 0% 1%
	Nuitka	2.90	2.91	10,532	322	1% 1% 1% 100%
	Nuitka	2.90	2.91	10,548	322	67% 1% 34% 1%
	Cython #3	1.95	1.95	10,816	639	99% 2% 2% 2%
	Cython #2	1.07	1.07	10,876	364	6% 1% 98% 1%
	Python 3 #2	1.08	1.08	10,956	389	64% 13% 42% 6%
	Cython #2	1.05	1.05	10,984	364	1% 3% 1% 100%
	Cython #3	1.95	1.95	11,008	639	100% 1% 2% 1%
	Cython #2	1.06	1.06	11,036	364	100% 3% 3% 1%
	Python 3 #2	1.07	1.12	11,128	389	6% 100% 1% 1%
	Python 3 #2	1.13	1.14	11,152	389	17% 18% 23% 100%
	Python 3 #5	0.99	0.99	11,172	710	25% 6% 77% 4%
	Cython #3	1.95	1.95	11,276	639	2% 1% 100% 2%
	Python 3 #5	0.98	0.98	11,292	710	2% 5% 100% 3%
	Python 3 #5	0.98	1.03	11,296	710	2% 100% 2% 5%
	Python 3 #3	2.05	2.06	11,384	664	60% 12% 49% 8%
	Python 3 #3	2.13	2.14	11,388	664	13% 19% 15% 100%
	Python 3 #3	1.96	1.97	11,416	664	100% 6% 3% 2%
	Nuitka #3	1.96	1.96	12,160	664	2% 100% 5% 3%
	Nuitka #2	1.02	1.03	12,264	389	3% 5% 1% 97%
	Nuitka #3	1.95	1.95	12,268	664	2% 3% 2% 100%
	Nuitka #2	1.05	1.05	12,284	389	1% 100% 0% 2%
	Nuitka #3	1.94	1.95	12,292	664	100% 1% 2% 1%
	Nuitka #5	0.96	0.96	12,308	710	100% 1% 2% 1%
	Nuitka #2	1.02	1.02	12,432	389	0% 100% 1% 0%
	Nuitka #5	0.95	0.96	12,480	710	2% 100% 0% 0%
	Nuitka #5	0.95	0.96	12,484	710	0% 1% 100% 1%
	RustPython	3.97	4.08	20,344	322	45% 30% 81% 26%
	RustPython	3.52	3.52	20,400	322	8% 12% 3% 91%
	RustPython	3.52	3.52	20,664	322	8% 32% 3% 70%
	Pyston	0.91	0.91	26,480	322	100% 0% 0% 1%
	Pyston	0.90	0.90	26,528	322	1% 100% 0% 0%
	Pyston	0.91	0.91	26,560	322	0% 4% 95% 0%
	IronPython	13.12	12.68	77,680	322	58% 2% 41% 2%
	IronPython	13.14	12.69	78,604	322	0% 57% 3% 42%
	PyPy 3	3.53	3.64	79,024	322	49% 77% 89% 59%
	PyPy 3	3.59	3.63	79,088	322	98% 75% 45% 52%
	IronPython	13.15	12.69	79,276	322	1% 29% 5% 68%
	PyPy 3	3.54	3.73	79,340	322	70% 97% 68% 39%
	PyPy 2	3.00	3.03	85,776	322	6% 11% 99% 8%
	PyPy 2	3.08	3.15	86,036	322	10% 17% 100% 10%
	PyPy 2	3.32	3.34	86,360	322	32% 32% 31% 92%
	Jython	8.84	5.40	290,952	322	34% 45% 51% 35%
	Jython	8.86	5.44	294,580	322	29% 51% 30% 54%
	Jython	9.42	5.63	324,056	322	47% 39% 41% 40%
missing benchmark programs
	Shedskin	No program
	Numba	No program
	Grumpy	No program
	Graal	No program

 pidigits benchmark : Streaming arbitrary-precision arithmetic

diff program output N = 27 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 use the same step-by-step spigot algorithm to calculate digits of Pi.

Each program should

• calculate the first N digits of Pi
• print the digits 10-to-a-line, with the running total of digits calculated

Programs should adapt the step-by-step algorithm given on pages 4,6 & 7 of Unbounded Spigot Algorithms for the Digits of Pi (156KB pdf). (Not the deliberately obscure version given on page 2.)(Not the Rabinowitz-Wagon algorithm.)

In addition to language specific multiprecision arithmetic, we will accept programs that use GMP.

For more information see Eric W. Weisstein, "Pi Digits." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/PiDigits.html

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