Hacker News Reader

Comment by aqme28 | Hacker News Reader

I am currently working on a project trying to convert some numerical linear algebra from Fortran90 into Python and was frankly shocked at how much more performant some relatively naive Fortran is, even over using Numpy/Scipy for matrix operations. Orders and orders of magnitude faster. No, I still don't totally understand why.

View on HN

I would absolutely expect python to perform orders if magnitude slower. Even optimal python calling numpy is going through multiple levels of abstraction copying data around. Fortran is raw operations, compiled and with the data packed together.

I would strongly suspect that you are misusing Numpy in some way.

Python is horrifically slow, everyone knows that, but if used correctly numpy can even the playing field a great deal. I've seen lots of python/numpy written using nested loops, and this is always going to be really really slow.

https://github.com/thornewolf/CFC-Fortran

This is somewhat of a link dump since the code is definitely too hard to interpret for someone not familiar but this is me converting some fairly performant Fortran code to python w/ numba at only a 1-2% performance penalty.

Why port? Just make it tidier and bind it with f2py so you can use the kernels together with numpy in your glue.

You're probably doing some unnecessary data movement. Using a modern profiler like scalene might point out where some improvements are possible: https://github.com/plasma-umass/scalene

lots of avoidable copies in NumPy, and even if you manage to get rid of that, the python interpretation overhead isn't amortized until your matrix dimensions are in the several-hundreds at least.

sorry but maybe give Julia a try?

Did actually switch to that. Surprisingly still a lot less performant than the Fortran code, but at least workable.

make a question post on Julia discourse, many people including me are more than happy to do free work for you!

Reply on news.ycombinator.com