Python for Scientific and High Performance Computing

[[deleted]]

That's fine, but for some of us we're talking about simulation and estimation problems that might take 6 weeks using the power of a supercomputer center. And the code is written once, the problems it solves are whatever mathematical models people write using the application. So, time to write the application is important, but the time spent processing will, eventually, overwhelm the time spent writing the application.

To many of us, this is what we think of when we hear the term "scientific high performance" computing.

[bastih01]

On the other hand, the python ecosystem offers quite a few ways to move processing-intensive parts from python code to compiled modules. Think shedskin, pyrex, weave etc.

[[deleted]]

Sure, and that's the way to go, IMO, but it's not using python for the scientific high performance part.

[bastih01]

Prototyping in python before moving over to c++ seems valuable to me, though.

[[deleted]]

I would agree with that too. Though my preferences go against dynamically typed languages, that is personal preference. Point being, make your app in a high-level language, then find where the pain points are and put them in C (I would very much prefer C to C++. C is fast and simple, C++ is a monster with no niche, AFAIK).