I'm following this with interest --
Just to note, performance, especially concurrent performance, may not be an issue in many use cases. Rather, it's compatibility. And, to an extent, a deployment issue. For example, I found myself relying on middleware (a task queue) to allow a Jython/JVM deployment to run code that would only work in CPython. Speed is not the issue -- but it would be nice to be able to run the tasks straight from the JVM and not have to require middleware plus a CPython deployment.
As you point out, there are reasonable tradeoffs.
[crossposting to jython-dev]Because of some conversations I had with Maciej (mostly at Folsom Coffee in Boulder :) ), we are considering adding support for the CPython C-Extension API for Jython, modeling what has already been done in PyPy and IronPython. Although I think it may make a lot of sense to port NumPy to Java, and have argued for it in the past, being pragmatic suggests it's better to work with the tide of NumPy/Cython than against it. Also, this can bring in a large swath of existing libraries to work with Jython, including those coded against SWIG, at the cost that it will not run under most security manager policies. I think that's a reasonable tradeoff.Similar concerns that Maciej raises apply to Jython. No Java JIT will inline such native code, marshaling from the Java domain to the native one will be expensive, etc. But this is (mostly) true of Jython today, from Python code to Java (although invokedynamic will at least reduce some of those costs). But users can still take advantage of Java to achieve much better performance from Jython, if they are careful about structuring the execution of their code. At the end of the day, Jython to C code, including that produced by Cython should see a similar performance profile to CPython to C code, as long as they don't hammer the INCREF/DECREF *functions*. (JRuby is implementing something similar, and we probably can borrow their "refcounting" support.) But of course that's exactly what one needs to avoid to write performant extension code anyway in CPython, at least if it's to be multithreaded.One interesting part of this discussion is whether we can support lock eliding. This is one part of JIT inlining that you don't want to give up for multithreaded performance. Rather than having C code callback into Java to release the GIL (which is only global for such C code!), it would be better to have a marker on the C code that allows for immediate release, or perhaps some other inlinable Java stub. I could imagine this could be readily supported by Cython (and perhaps already is).Lastly, I want to emphasize again that if/when Jython adds support for the C extension API, the "GIL" and "refcounting" support will only be for such C code! We like our concurrency support and we are not giving it up :)- JimOn Thu, Aug 12, 2010 at 3:25 AM, Stefan Behnel <firstname.lastname@example.org> wrote:
Maciej Fijalkowski, 12.08.2010 10:05:
> On Thu, Aug 12, 2010 at 8:49 AM, Stefan Behnel wrote:
>> there has recently been a move towards a .NET/IronPython port of Cython,
>> mostly driven by the need for a fast NumPy port. During the related
>> discussion, the question came up how much it would take to let Cython also
>> target other runtimes, including PyPy.
>> Given that PyPy already has a CPython C-API compatibility layer, I doubt
>> that it would be hard to enable that. With my limited knowledge about the
>> internals of that layer, I guess the question thus becomes: is there
>> anything Cython could do to the C code it generates that would make the
>> Cython generated extension modules run faster/better/safer on PyPy than
>> they would currently? I never tried to make a Cython module actually run on
>> PyPy (simply because I don't use PyPy), but I have my doubts that they'd
>> run perfectly out of the box. While generally portable, I'm pretty sure the
>> C code relies on some specific internals of CPython that PyPy can't easily
>> (or efficiently) provide.
> CPython extension compatibility layer is in alpha at best. I heavilyIf you only use it to call into non-trivial Cython code (e.g. some heavy
> doubt that anything would run out of the box. However, this is a
> cpython compatiblity layer anyway, it's not meant to be used as a long
> term solutions. First of all it's inneficient (and unclear if will
> ever be)
calculations on NumPy tables), the call overhead should be mostly
negligible, maybe even close to that in CPython. You could even provide
some kind of fast-path to 'cpdef' functions (i.e. functions that are
callable from both C and Python) and 'api' functions (which are currently
exported at the module API level using the PyCapsule mechanism). That would
reduce the call overhead to that of a C call.
Then, a lot of Cython code doesn't do much ref-counting and the like but
simply runs in plain C. So, often enough, there won't be that much overhead
involved in the code itself either, especially in tight loops where users
prune away all CPython interaction anyway.
Well, unless both sides learn about each other, that is. It won't
> but it's also unjitable. This means that to JIT, cpython
> extension is like a black box which should not be touched.
necessarily impact the JIT, but then again, a JIT usually won't have a
noticeable impact on the performance of Cython code anyway.
Sure. That's why I asked if there is anything that Cython can help to
> Also, several concepts, like refcounting are completely alien to pypy
> and emulated.
improve here. For example, the code it generates for INCREF/DECREF
operations is not only configurable at the C preprocessor level.
This isn't only about making things fast when being rewritten. This is also
> For example for numpy, I think a rewrite is necessary to make it fast
> (and as experiments have shown, it's possible to make it really fast),
> so I would not worry about using cython for speeding things up.
about accessing and reusing existing code in a new environment. Cython is
becoming increasingly popular in the numerics community, and a lot of
Cython code is being written as we speak, not only in the SciPy/NumPy
environment. People even find it attractive enough to start rewriting their
CPython extension modules (most often library wrappers) from C in Cython,
both for performance and TCO reasons.
Native (standalone) C code isn't the goal, just something that adapts well
> There is another usecase for using cython for providing access to C
> libraries. This is a bit harder question and I don't have a good
> answer for that, but maybe cpython compatibility layer would be good
> enough in this case? I can't see how Cython can produce a "native" C
> code instead of CPython C code without some major effort.
to what PyPy can provide as a CPython compatibility layer. If Cython
modules work across independent Python implementations, that would be the
most simple way by far to make lots of them available cross-platform, thus
making it a lot simpler to switch between different implementations.
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