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Programming Python

See the PyPy JIT In Action 109

Posted by timothy
from the hey-those-are-good-cat-names dept.
derGoldstein writes "Project PyPy is an alternative implementation of Python, with the main advantage being a Just In Time (JIT) compiler which speeds up your code considerably. They've announced the first public release of jitviewer, which is a visualization tool that helps you understand how your code is being compiled by PyPy's JIT, all the way down to assembly. If you just want to see how it looks and play with it, they've set up an online demo — just select a file, and click 'Show Assembler.'"
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See the PyPy JIT In Action

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  • Go Pypy! (Score:4, Informative)

    by Anonymous Coward on Saturday August 13, 2011 @07:40PM (#37081770)

    I love python, and pypy makes dream about the day python could be used for anything, making java irrelevant.
    The language is readable, flexible, succinct, expressive and beautiful.
    Programming in python is more a joy than a core, but it has always suffered from a wide performance gap in cpu intensive tasks compared to traditional static languages.
    Hopefully, this is all about to change with projects like this.
    It's worth noting that Pypy is more than just an alternative python implementation. It is a framework for implementing jitted compiled dynamic languages.
    There are currently several works in progress with implementations of javascript, scheme, logo, php and other languages with pypy.

    • Good to know, I've toyed with the idea of trying to create a javascript jit, anyone who understands crockfordscript (which these days is practically everybody, making it all the more valueable) recognizes performance is the only thing missing from what could be one hell of a good language..
      • Uh, both Google's V8 and Mozilla's Tracemonkey are JIT engines. There's nothing wrong about creating your own, but if it's a question of speed and not about learning, sticking with V8 is probably a better option.

      • Javascript is not a nice language, it's the unholy chimera of Self and Java.

        Sure it's better than how most people describe it, but there are far better languages out there.

        • Alright, give me an alternative that does the same thing. I agree...JS is a dirty, problem-inducing (rather than problem solving) language. I just don't know what else I can use to move things around and cause the useful JS effects without a refresh without JS.

          • Self, Io-language, Smalltalk, Ruby and of course Python or even Lisp, but also Java, C# or Vala if you want static typing.

            You mean something supported on todays browsers? Yes, Javascript. But as long as you are reinventing the browser you can always pick a better scripting language.

            • Well, if I have the luxury of reinventing the browser and choosing a new standard, I would vote for Smalltalk (I would say Lisp, but I foresee forcing too many square pegs in round holes to go there). But right now we are stuck with JS or running things through the Java JIT.

    • Thanks anon!
      Somebody should mod this coward up.

    • Re: (Score:3, Insightful)

      by H0p313ss (811249)

      The language is readable, flexible, succinct, expressive and beautiful.

      I hear it cures cancer too. Oh wait, that's Ruby. (Actually, I love Python, but I'm allergic to fanboism...)

      • by Tumbleweed (3706) *

        I hear it cures cancer too. Oh wait, that's Ruby. (Actually, I love Python, but I'm allergic to fanboism...)

        While it's true that Ruby cures (certain types of) cancer, it also has the unfortunate side effect of causing autism. Don't believe me? Try reading non-Ruby code written by Ruby fans.

      • by sgt scrub (869860)

        Your just scared because it allows one to quickly migrate U235.XML to U238.XML.

    • Re: (Score:3, Insightful)

      by Anonymous Coward

      Python is not going to make Java irrelevant. Sorry. I know it hurts to be told that your religion will never become universal, but that's reality for you.

      Certainly one reason for choosing Java over Python today is performance. Python is relatively slow, and sometimes throwing more hardware at the problem is not an option, and rewriting stuff in C (like Python fanboys always suggest) is not actually that easy (and brings with it all the problems of writing anything in C -- there's a reason we want to use

      • The whole Java *or* Python discussion is irrelevant. You can use Jython, and take advantage of all the Python and Java libraries.

        • The whole Java *or* Python discussion is irrelevant. You can use Jython, and take advantage of all the Python and Java libraries.

          Did you not read a word that this guy wrote, or did you simply not understand any of his points?

          The weaknesses that he pointed out did not deal with Python "lacking access to Java libraries". The weaknesses that were pointed out included Python's lack of compile-time type-checking, lack of real publication and enforcement of API contracts, and lack of lexical scope.

          Jython addresses none of that. It does not magically transform Python from a hacky Perl-successor into a language broadly suitable for large s

      • Re: (Score:2, Insightful)

        by OeLeWaPpErKe (412765)

        You're argument only makes sense if you accept that the python language design requires dynamic typing. And this may (may) be true for the really advanced and esoteric uses of python, but anyone who's ever built a tiny compiler knows that 95% of python can easily be implemented statically typed. It would be a great exercise to try to fully inline the entire python runtime library to cover the remaining 5%.

        Personally I'm convinced that a statically typed python supporting 90% of common use cases, and the IDE

        • by chromatic (9471)

          [T]here is no theoretical problem predicting all allocations of a python program at compile time...

          ... until you do IO.

        • So you are claiming that duck typing is not used in practice other than in esoteric projects. That is a little bit naive, consider why would something so complex be left in the language design if it was not necessary? The entire language relies on duck typing being there to make some other design decisions easier to make, for example all containers are polymorphic collections rather than the monomorphic collections in almost every other language. Your fragment of code is a single instance where forward inte

      • [...] and rewriting stuff in C (like Python fanboys always suggest) is not actually that easy (and brings with it all the problems of writing anything in C -- there's a reason we want to use Python or Java in the first place, right?)

        Actually, dismissing writing parts in C is not so simple. The only problem I'm aware of is the portability. And even that is handled by package/module management automatically.

        Otherwise, most problem-free C programming I ever done (as C stuff goes) was Perl XS (*). Because you can leave to Perl all the mundane tasks and implement in C only the most performance-critical parts, which often have well defined input and well defined output. Even memory allocation stops being a problem: the objects one alloca

    • by KDR_11k (778916)

      Is PyPy very different from Psyco? I've been using the latter for scripts that needed speedups.

      • by Ragzouken (943900)

        From what I understand, Psyco is the father of PyPy. Psyco is a finished project, and the things learnt from it are being developed and refined in the PyPy project.

      • by rwa2 (4391) *

        I used psyco for my thesis back in 2007. It improved my SimPy runtimes by a factor of 100, merely with a simple "import psyco" in the beginning of the project!

        Unfortunately psyco only works on 32-bit architectures, and after I got my degree I finally upgraded to a 64-bit system and sat on my work waiting for something to happen. Now I guess I finally need to pull it out again and see what's up.

  • by Anonymous Coward on Saturday August 13, 2011 @07:40PM (#37081776)

    the connection has timed out

    Python, the new Java

  • Woo hoo! (Score:4, Informative)

    by 93 Escort Wagon (326346) on Saturday August 13, 2011 @07:53PM (#37081822)

    I'm glad to see it scales well!

  • by jcr (53032)

    Someone remind me... Isn't PyPy the implementation that's based on LLVM?

    -jcr

  • Quora (Score:2, Interesting)

    by Anonymous Coward

    quora.com is now running on PyPy.

    http://www.quora.com/Alex-Gaynor/Quora-product/Quora-is-now-running-on-PyPy

  • Faster than C? (Score:4, Informative)

    by lee1 (219161) <lee@@@lee-phillips...org> on Sunday August 14, 2011 @12:50AM (#37082974) Homepage

    Not just faster than CPython, but faster than C [blogspot.com] for some common tasks. Pretty amazing.

    However, this project is not yet very useful to the people who might be most interested in a really fast python, as it does not work with numpy [blogspot.com]. But when they get that to work, wow.

    • Dream on, its like those 'over unity' energy source claims.
    • by nzac (1822298)

      Did you read the comments?
      That's not a common task if he created a file with a meaningful output it would be a good benchmark.

      This looks to either be a stumbled on case or something contrived. He used -O4 for crying out loud (it's treated as -O3) there could be a bug from excessive optimization and the main function sprintf cannot be optimized as its a pre complied library (i could be wrong here).

      The result is best summed up by the comment:

      PyPy does nothing 1.9 times faster than C.

      • by lee1 (219161)
        As he says himself, "the sprintf function lives in libc and so cannot be specializing over the constant string, which has to be parsed every time it's executed. In the case of PyPy, we specialize the assembler if we detect the left hand string of the modulo operator to be constant."
        • by nzac (1822298)

          So what they are really saying is pypy is faster than a function in glibc for a task that glibc would never be used for (i would think you chose a modern library if you wanted to do this in some real code).
          This is benchmarking not against C but a bloated and not particularly optimized library.

          • by m50d (797211)
            Huh? How else would you do the given task in C? And glibc is the standard C library on most current linucies. It's absolutely a relevant comparison.
            • by nzac (1822298)

              If you were going to do this as a one off sure you would use libc if you are going to do the same thing 10000 times in a row as you entire program and care about performance you would look for something better (this would likely be libboost in C++).

              For anything to do with strings python is far more expressive than c and this case could be the more elegant and robust solution (except for memory usage). For this example i guess you 'parse' (not sure if that's the correct term in C) the text before the looping

              • by m50d (797211)
                We're talking C, not C++, so boost is irrelevant. Sure sprintf is big and flexible, but so is python's % operator; the comparison is a fair one, standard language feature to standard language feature. You may well have a faster string library, but using nonstandard libraries has its own problems - you now have two different ways to format your strings, and if you try and do the same thing with some other part of your program (say introduce a nonstandard faster networking library) then whoops, your two nonst
                • by nzac (1822298)

                  We are talking about pypy being better than native programs so if C++ benchmarks are also valid. If this is just a contest to convert an integer to a string and write it to arbitrary memory as fast as you can and not factoring in pythons order of magnitude(s) greater expressiveness and readability. You are taking a standard data type and returning an array of standard data there no issues with other libraries. Using a copy and pasted integer to string function (itoa(...)) that has some checking of valid inp

    • Also, it needs to be able to call C and Fortran libraries to be really useful in NumPy. Because no one is going to rewrite all of those numerical libraries.
  • by Animats (122034) on Sunday August 14, 2011 @12:51AM (#37082980) Homepage

    It's an impressive effort that they were able to get this to work at all. Python is really tough to optimize. All bindings are changeable at any time, even from another thread. (The latter is silly, but since it doesn't cost anything to do that in CPython, which is an inefficient naive interpreter that can only run one thread at a time and spends much of its time doing dictionary lookups. Von Rossum defines the language by what CPython can do. There's a huge speed penalty for allowing such extreme dynamism, about 60x over C/C++. The Google team tried to fix that with Unladen Swallow, but gave up when their JIT system was barely faster than CPython.)

    PyPy's most effective optimization to date is that it figures out when numbers don't have to be boxed. This allows generating numeric machine code, rather than grinding through the object machinery for every number. They have to be prepared to discard code when something binding gets changed. This requires a very complex system, involving two interpreters (regular and backup) as well as the JIT compiler.

    The PyPy crowd is at last starting on the tougher optimizations, like hoisting some operations out of loops. (FORTRAN compilers were doing this in the 1960s.) That's real progress, but it's very hard to do in such a dynamic language.

    Many of the optimizations involve generating run-time code that checks to see if the normal case is occurring, and that no other code has patched the code or changed the data from the outside in a way that invalidates the fast path. Then there's code to unwind what the fast path was doing, and interpret or recompile. Most such code is never executed.

    • by jgrahn (181062)

      There's a huge speed penalty for allowing such extreme dynamism, about 60x over C/C++.

      That number is surely made up; perhaps it applies to some specific set of benchmarks? In reality the factor is going to vary widely depending on the problem. And oh, you can't lump C and C++ together and call them C/C++.

      • It matches what I've seen. I've written compilers, static analysers and visualisation code in Python. In most cases there is about a 100x difference in speed between the Python code and C code to implement the same algorithm. Python is still useful for prototyping in as most of that code could be written 5x to 10x more quickly than the C code.

    • I noticed that their progress seemed to have stopped, is there any official announcement?

      The Google team tried to fix that with Unladen Swallow, but gave up when their JIT system was barely faster than CPython.)

      • by Animats (122034)

        I noticed that their (Unladen Swallow) progress seemed to have stopped, is there any official announcement?

        Unladen Swallow is an ex-parrot. [google.com]. "Jeffrey and I have been pulled on to other projects of higher importance to Google.", writes the former project lead.

    • Lisp yawns at how easily impressed you are.

  • And now for ruby please. I love ruby, but the performance issues drive me crazy sometimes.

    • by Moldiver (1343577)

      Get a Mac and have a look at MacRuby. Near Obj-C Performance and JIT/AOT through LLVM.

  • I'm sorry...I don't want to see how it looks or play with your PyPy. I will most definitely NOT click on your online demo to show your "Assembler".

  • Seams boxing isn't too much of an issue for vanilla python when summing 1000,000 integers.

    $ pypy -m timeit "sum(range(1000000))"
    10 loops, best of 3: 55.2 msec per loop
    $ python -m timeit "sum(range(1000000))"
    10 loops, best of 3: 71.5 msec per loop
    $ python3 -m timeit "sum(range(1000000))"
    10 loops, best of 3: 62.7 msec per loop
    $ python -m timeit "sum(xrange(1000000))"
    10 loops, best of 3: 26.6 msec per loop
    $ pypy -m timeit "sum(xrange(1000000))"
    10 loops, best of 3: 132 msec per loop

    $ pypy --version
    Python 2.7.1

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