Re:Introduction to Python for Computational Science and Engi...
by I_deny_it on Friday November 25, 2016 @11:17PM
(#13041693)
If you really want to run faster, you should try a new language. 30-40% for multiprocessing is nowhere near the benefit you'd get from a single threaded implementation in C, C++, Java, Rust, Ocaml, Go, or even JavaScript or LuaJIT. Those runs between 10 and 1000 times faster (1000% - 100000%) than Python.
Re:Introduction to Python for Computational Science and Engi...
by dagw on Saturday November 26, 2016 @02:45AM
(#13042368)
Those runs between 10 and 1000 times faster (1000% - 100000%) than Python.
They're faster than pure python in the general case, but once you throw numpy, numba, numexpr and cython into the mix and focus on numeric workloads, most of that difference disappears.
Re:Introduction to Python for Computational Science and Engi...
by _Wintermute on Saturday November 26, 2016 @04:56AM
(#13042766)
For scientific computing I can't see many people making use of python's multiprocessing library. A lot of the time you're writing scripts that you might run a handful of times at most, or if speed is an issue you write it in c++.
Sir/Madam, I am here to make you happy then. Python 3's multiprocessing library is leaps and bounds "better" than python 2's. By "better", I mean faster(in my workloads) by 30-40%. If time is money, then that feature alone saves you both.