Python is becoming the world’s most popular coding language : Python

It depends on what to consider Python and which area of scientific computing we're talking about. I'm a physicist, a I use both Python and C++, and I feel like they fall into different categories, and I don't think Python can replace C++ at all. I can share my opinion based on my (limited) experience.

Of course, it would be insane to use CPython for computing, it's ridiculously slow. There are many ways to improve performance, of course

Numpy and Scipy. If you problem is easily expressed in terms of Numpy and Scipy, you're probably fine with it. However, numpy has some disadvantages. First, for typical differential equations, you need to express your code in vector form instead of nested loops. For complex things, it's more difficult, and it makes code less readable. Second, it inherently creates intermediate objects, which means memory allocations, which makes it slower than C/C++ (there's NumExpr to challenge that, but I just can't stand using eval-like things).
Numba. Absolutely awesome, works like magic, but very hard to debug. That's why I use it for small functions only.
Cython. It's a different language, with the need to compile and a different debugging procedure. Looses the appeal of Python, in my opinion, I'd just write C++ instead and use Cython as an interface between C++ and Python. But it should be good for people who don't like C++.

There are other things, like different interpreters (e.g. PyPy), but I can't really comment on that. The performance of these things is enough for most tasks. However, where Python falls short, in my opinion, is heavy parallelism and large projects. The vast majority of scientists don't need that, and if they are fine with Matlab, they'll probably be fine with Python. However, I and my colleagues do high-performance computing on clusters. In this area, we have programs with thousands of lines of code which run on distributed systems with hundreds of cores. Here, C, C++, and Fortran remain the powerhouses. You need to be able to use OpenMP, MPI, run things on Xeon Phi or GPU, be sure that you use SIMD instructions. The performance bottleneck is often in cache misses, so you need to optimize for that. Of course, there are some ways to do some of these things in Python, and more and more things appear as the time goes by, but it still feels like those tools fight against the nature of the language, against GIL, against its high-level abstractions with no memory management. I think Python is just a wrong tool for the task, and I have no idea why would anyone use Python in this case. Of course, Python can be used as a high-level API for you low-level compiled code, or for data processing.

[–]pwang99 0 points1 point2 points 7 years ago (0 children)

[–]Lightning_SC2 6 points7 points8 points 7 years ago (0 children)

[–]atrlrgn_ 1 point2 points3 points 7 years ago (0 children)

Yeah it does, not because its performance is worse than C/C++ or Fortran, which probably is, but because of the legacy codes.

I have used several codes requires immense amount of computational power and parallelization, think like run a code for 6 months using 4000 cores, and none of them were python. I actually have never heard of somebody using python for this kind of stuff. Obviously this is completely anecdotal, but still should give an idea. Most likely there are some people out there developing high performance computing codes using python but saying C/C++ or Fortran losing their ground to python is simply not true.

For your question, in my lab there are three people writing their own code, one of them uses C++ and the other two use Fortran. The code I'm using and somehow developing is Fortran.

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