Anthony Flury via Python-list schrieb am 21.12.18 um 09:06: > I thought I would look at a side by side comparison of CPython, nuitka and > PyPy
Interesting choice. Why nuitka? > *The functionality under test* > > I have a library (called primelib) which implements a Sieve of Erathoneses > in pure Python - it was orginally written as part of my project Euler attempts > > Not only does it build a sieve to test primality, it also builds an > iterable list of primes, and has functionality to calculate the prime > factors (and exponents) and also calculate all divisors of a given integer > (up to the size of the sieve). > > To test the primelib there is a simple harness which : > > * Builds a sieve for integers from 2 to 104729 (104729 is the 10,000th > prime number) > * Using a pre-built list from primes.utm.edu - > o For every integer from 2 to 104729 the prime sieve and pre-built > list agree on the primality or non-primality > o confirm that the list of ALL primes identified by the sieve is > the same as the pre-built list. > o For every integer from 2 to 104729, get primelib to generate the > prime factors and exponents - and comfirm that they multiply up > to the expected integer > o For every integer from 2 to 104729 get primelib to generate the > divisors on the integer, and confirm that each divisor does > divide cleanly into the integer > > The Sieve is rebuilt between each test, there is no caching of data between > test cases, so the test harness forces a lot of recalculations. > > I have yet to convert primelib to be Python 3 compatible. > > Exactly the same test harness was run in all 3 cases : > > * Under CPython 2.7.15, the execution of the test harness took around > 75 seconds to execute over 5 runs - fastest 73, slowest 78. > * Under Nuitka 0.6, the execution of the test harness after compiler > took around 85 seconds over 5 runes, fastest 84, slowest 86. > * Under PyPy, the execution of the test harness took 4.9 seconds on > average over 5 runs, fastest 4.79, slowest 5.2 > > I was very impressed at the execution time improvement under PyPy, and a > little surprised about the lack of improvement under Nuitka. > > I know Nuitka is a work in progress, but given that Nuitka compiles Python > to C code I would have expected some level of gain, especially in a maths > heavy implementation. It compiles to C, yes, but that by itself doesn't mean that it makes it run faster. Remember that CPython is also written in C, so why should a simple static translation from Python code to C make it run faster than in CPython? Cython [1], on the other hand, is an optimising Python-to-C compiler, which aims to generate fast code and allow users to manually tune it. That's when you start getting real speedups that are relevant for real-world code. > This comparison is provided for information only, and is not intended as > any form of formal benchmark. I don't claim that primelib is as efficient > as it could be - although every effort was made to try to make it as fast > as I could. I understand that it came to life as an exercise, and you probably won't make production use of it. Actually, I doubt that there is a shortage of prime detection libraries. ;) Still, thanks for the writeup. It's helpful to see comparisons of "code how people write it" under different runtimes from time to time. Stefan [1] http://cython.org/ -- https://mail.python.org/mailman/listinfo/python-list
