4 ms·
I just made a quick test: CPython-3.6.1 vs. Jython-2.7.0 (May 2015) I ran Larry Hastings' Gilectomy testprogram x.py: fib(40) on 8 threads HW: MacBook Pro, 201
by alfanerd 9y ago
I just made a quick test: CPython-3.6.1 vs. Jython-2.7.0 (May 2015)
I ran Larry Hastings' Gilectomy testprogram x.py: fib(40) on 8 threads
HW: MacBook Pro, 2015, 8 (4+4) cores, 1Gb RAM
Jython ran the program 8 times faster, utilising all 8 cores >95%. Python ran on 1-2 cores less than 60% utilisation. (Pretty sure Jython will run 16 times faster on 16 cores)
It's 2017, why this is acceptable to GvR and the Python community is beyond me.
Jython:
real 1m4.959s
user 7m38.521s
sys 0m2.396s
Python:
real 8m19.035s
user 8m16.508s
sys 0m11.424s
- zepolen 9y agoCould you post the code for that fib program I couldn't find it anywhere.
- alfanerd 9y agoIt is in the Gilectomy branch of Larry Hastings github project. (https://github.com/larryhastings/gilectomy https://github.com/larryhastings/gilectomy) I also pasted the fib test to pastebin: https://pastebin.com/Ryyb2K7V https://pastebin.com/Ryyb2K7V
- zepolen 9y agoAh so just the naive recursive fibonacci on 8 threads with no data sharing between them. Interestingly doing the same on Cpython using the multiprocessing module was ~2x slower than jython/threads. More interestingly pypy with multiprocessing was ~5x faster than jython/threads. $ time jython fib.py 40 real 1m11.247s user 6m14.130s sys 0m3.012s $ time python fib.py 40 real 2m4.067s user 11m46.103s sys 0m2.352s $ time pypy fib.py 40 real 0m21.040s user 1m51.461s sys 0m1.892s