Why I Still Use Python for High Performance Scientific Computing
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Why I Still Use Python for High Performance Scientific Computing
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Re: Why I Still Use Python for High Performance Scientific Computing
#2Summary in the conclusion:
"The end result is an implementation several orders of magnitude faster than the current reference implementation in Java. ... [Python] makes the first version easy to implement and provides plenty of powerful tools for optimization later when you understand where and how you need it." [edited to be a statement instead of rhetorical question]
Re: Why I Still Use Python for High Performance Scientific Computing
#3Good article. Couldn't find who wrote it since it doesn't have a byline. I'm guessing it was Leland McInnes?
Re: Why I Still Use Python for High Performance Scientific Computing
#4Why isn't Haskell, or any other functional language, popular for this sort of thing? Turning A into B is what FP excels at, and you shouldn't have to reason about side effects, besides writing the graph images somewhere.
From what I've heard from a friend of using other people's code in one particular scientific field (stringly type some of the things, probably accidentally, don't document this), an at-least-passable type system would be a huge improvement.
Re: Why I Still Use Python for High Performance Scientific Computing
#5Why isn't Haskell, or any other functional language, popular for this sort of thing? Turning A into B is what FP excels at, and you shouldn't have to reason about side effects, besides writing the graph images somewhere. From what I've heard from a friend of using other people's code in one particular scientific field (stringly type some of the things, probably accidentally, don't document this), an at-least-passable…
The only functional langauges to gain much popularity here are OCaml and F#.
Haskell tends not to be a go-to choice here because it... well... there are a lot of reasons. It's a tough language to learn compared to its competitors because it's basically a few really awesome modern features in a massive graveyard of failed academic initiatives that are now enshrined in the lore of the language because one or two useful libraries used them.
Re: Why I Still Use Python for High Performance Scientific Computing
#6Why isn't Haskell, or any other functional language, popular for this sort of thing? Turning A into B is what FP excels at, and you shouldn't have to reason about side effects, besides writing the graph images somewhere. From what I've heard from a friend of using other people's code in one particular scientific field (stringly type some of the things, probably accidentally, don't document this), an at-least-passable…
Probably because functional languages don't actually at excel scientific computing relative to Python, C#, Java, and JavaScript. :D
Edit: I kid, but most of the languages I just mentioned have very fast native compilers, easy ways of invoking low-level interop, functional-style libraries if you want them, and (except for Python) C-like syntax making it easy to cut-and-paste.
Re: Why I Still Use Python for High Performance Scientific Computing
#7Agreed. Python is an excellent tool in that respect. Batteries included helps. Being able to access fast C routines help. Compile to C projects like Numba and Cython also help. And of course, ipython (Jupyter) notebooks for exploration.
Re: Why I Still Use Python for High Performance Scientific Computing
#8Summary in the conclusion: "The end result is an implementation several orders of magnitude faster than the current reference implementation in Java. ... [Python] makes the first version easy to implement and provides plenty of powerful tools for optimization later when you understand where and how you need it." [edited to be a statement instead of rhetorical question]
All the points the author makes through the post are interesting, and Python is definitely great for protoyping, but I think the initial premise is false:
"people don't tend to think of [Python] as a high performance language; for that you would want a compiled language -- ideally C or C++ but Java would do."
Java is compiled to bytecode, but it isn't a "compiled language" since that bytecode has to be interpreted by the JVM. All the good libraries the author mention are probably implemented in C or FORTRAN, and so a true implementation in C with the right compiler optimizations would for sure be faster than the python code.
Re: Why I Still Use Python for High Performance Scientific Computing
#9Summary in the conclusion: "The end result is an implementation several orders of magnitude faster than the current reference implementation in Java. ... [Python] makes the first version easy to implement and provides plenty of powerful tools for optimization later when you understand where and how you need it." [edited to be a statement instead of rhetorical question]
Actually, even the subsection headings in bold give a very succinct summary:
- Python has easy development (https://xkcd.com/353/)
- Great libraries (ie, free matlab)
- Cython for efficiency via C
- The algorithms themselves determine speediness (ie numerical methods)
Re: Why I Still Use Python for High Performance Scientific Computing
#10Why isn't Haskell, or any other functional language, popular for this sort of thing? Turning A into B is what FP excels at, and you shouldn't have to reason about side effects, besides writing the graph images somewhere. From what I've heard from a friend of using other people's code in one particular scientific field (stringly type some of the things, probably accidentally, don't document this), an at-least-passable…
The large number of high quality scientific and computational libraries that are easily available for Python (most things are a "conda install" or "pip install" away) plus the Jupyter (literate programming) notebook (although that now supports other languages) currently make a very compelling case for using Python instead of Haskell and most other functional languages.