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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

#11

Post by andylynch »

vmarsy wrote: post #8
msellout wrote: post #2

Summary 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…

Java(well, Sun's Hotspot) is most definitely compiled, but mostly by the JIT at runtime. The interpreter only runs for the the first warmup, usually 10,000 method calls and the JIT kicks in and translates the method to machine code. The tricky part is it's not simple to control exactly how and when it does its job, although it usually works very well.

Re: Why I Still Use Python for High Performance Scientific Computing

#12

Post by tokipin »

optimiz3 wrote: post #6
cballard wrote: post #4

Why 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.

No need to kid. Self-described "FP" languages are a pain in the ass for the type of programming scientists do. I don't even know what kind of programming those languages aren't a pain in the ass for.

Re: Why I Still Use Python for High Performance Scientific Computing

#13

Post by fsloth »

tokipin wrote: post #12
optimiz3 wrote: post #6

Earlier quoted context omitted.

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.

No need to kid. Self-described "FP" languages are a pain in the ass for the type of programming scientists do. I don't even know what kind of programming those languages aren't a pain in the ass for.

"I don't even know what kind of programming those languages aren't a pain in the ass for."

If we limit the discussion to ML languages, they are generally very nice e.g. for datastructure transforms (including interpreters and compilers). Static typing with type inference makes implementations short, and, if written in good style, are correct when they compile.

There are various other reasons why one would not want to use them. For me it's platform stability and availability - while I enjoy functional languages in principle - I yet seem to choose C++ more often than not for my hobby projects.

I would say learning self-described fp-languages have made me a better programmer who values static typing more than before.

Re: Why I Still Use Python for High Performance Scientific Computing

#14

Post by reverius42 »

tokipin wrote: post #12
optimiz3 wrote: post #6

Earlier quoted context omitted.

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.

No need to kid. Self-described "FP" languages are a pain in the ass for the type of programming scientists do. I don't even know what kind of programming those languages aren't a pain in the ass for.

I could make the same assertion about "self-described OOP languages" or "self-described procedural languages" with just as much evidence. Once you've done it enough, all programming feels like a pain in the ass.

I see FP languages being able to excel at the kind of projects with large, maintainable codebases that evolve over a decade and are shared between dozens of developers. Service oriented architectures serve the same goal -- isolating behavior and state locally, and being explicit about mutation and state changes over time.

Re: Why I Still Use Python for High Performance Scientific Computing

#15

Post by Tomte »

cballard wrote: post #4

Why 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…

Because the Python ecosystem is huge, with real scientists writing real libraries to get stuff done.

The Haskell crowd seems to write monad tutorials that are either cute or unintellegible, and stratosphere-high level stuff where I wouldn't have the slightest clue what I can use them for (Arrows? Zippers?).

Re: Why I Still Use Python for High Performance Scientific Computing

#16

Post by msie »

Tomte wrote: post #15
cballard wrote: post #4

Why 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…

Because the Python ecosystem is huge, with real scientists writing real libraries to get stuff done. The Haskell crowd seems to write monad tutorials that are either cute or unintellegible, and stratosphere-high level stuff where I wouldn't have the slightest clue what I can use them for (Arrows? Zippers?).

And Lenses!

Re: Why I Still Use Python for High Performance Scientific Computing

#17

Post by kriro »

My current setup for "scientific computing" is RStudio and CSV files for quickly running typical stats-tests (a couple of t-tests and a tost + krippendorff's alpha the last couple of month) and python+libraries for anything that resembles "building stuff" (mostly scikit-learn to build some classifiers). I mostly use R "as a consumer" i.e. I basically use RStudio whenever my colleagues fire up SPSS. That combination works fairly well. I'd recommend it to anyone who enters academia in any field that involves statistics who doesn't want to use the typical proprietary tools (I've also tried PSPP and it works ok for basic tasks but lacks a lot of functionality. If all you want to do is run a quick t-test or ANOVA it's a decent tool).

Re: Why I Still Use Python for High Performance Scientific Computing

#18

Post by boulos »

If Numpy, Pandas, etc. were wrappable from JavaScript this could have easily been titled "Why I use Node.js for High Performance Scientific Computing".

The "Python" here isn't particularly material to the result, it's mostly a wrapper around C. Toss in Cython, and now you've really gone outside the bounds of "I'm just using 'Python' for HPC!".

I agree some of the tooling and niceties are beyond a doubt best in breed with Python, but it's disingenuous to equate this to "writing HPC code in Python". If you had written a RPython to Verilog translator that produced an FPGA of your algorithm would you call that "using Python"?

Re: Why I Still Use Python for High Performance Scientific Computing

#19

Post by vegabook »

boulos wrote: post #18

If Numpy, Pandas, etc. were wrappable from JavaScript this could have easily been titled "Why I use Node.js for High Performance Scientific Computing". The "Python" here isn't particularly material to the result, it's mostly a wrapper around C. Toss in Cython, and now you've really gone outside the bounds of "I'm just using 'Python' for HPC!". I agree some of the tooling and niceties are beyond a doubt best in breed…

Numpy can basically be considered to be part of the standard library for Python so I'd say using it still qualifies as "using Python". Pandas is newer but is graduating to that level of ubiquity too.

Re: Why I Still Use Python for High Performance Scientific Computing

#20

Post by ntoshev »

cballard wrote: post #4

Why 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 diff between Haskell and Python is not functional programming, I program functionally in Python, it is the type system.

This is subjective, but having tried Elm with its Haskellish type system, it seems that the type system makes me think hard about things that ultimately don't matter much. It might depend on your problem, but I doubt it. Even doing numerical computation with Theano I find myself better off just executing subexpressions and seeing results and the shape of the tensors interactively is more useful than thinking really hard how the operations transform the shape.

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