On Fri, Aug 19, 2011 at 7:35 PM, holger krekel <hol...@merlinux.eu> wrote: > Congrats also from here! > > On the plus side pytest's own test suite passes all tests, even some which > are marked as "expected-to-fail" with cpython-2.7. On the minus side, > the whole test run is still 2-3 times slower compared to cpython which is > slightly worse than with pypy-1.5. of course there is not too much to JIT but > still a bit of a dissappointing result.
Well, yes, but also we have no good answer to this. I think the reason why it got slower is because we compile functions now as well (which takes extra time). Test suites are hard I fear :( > > best, > holger > > On Thu, Aug 18, 2011 at 19:30 +0200, Maciej Fijalkowski wrote: >> ======================== >> PyPy 1.6 - kickass panda >> ======================== >> >> We're pleased to announce the 1.6 release of PyPy. This release brings a lot >> of bugfixes and performance improvements over 1.5, and improves support for >> Windows 32bit and OS X 64bit. This version fully implements Python 2.7.1 and >> has beta level support for loading CPython C extensions. You can download it >> here: >> >> http://pypy.org/download.html >> >> What is PyPy? >> ============= >> >> PyPy is a very compliant Python interpreter, almost a drop-in replacement for >> CPython 2.7.1. It's fast (`pypy 1.5 and cpython 2.6.2`_ performance >> comparison) >> due to its integrated tracing JIT compiler. >> >> This release supports x86 machines running Linux 32/64 or Mac OS X. Windows >> 32 >> is beta (it roughly works but a lot of small issues have not been fixed so >> far). Windows 64 is not yet supported. >> >> The main topics of this release are speed and stability: on average on >> our benchmark suite, PyPy 1.6 is between **20% and 30%** faster than PyPy >> 1.5, >> which was already much faster than CPython on our set of benchmarks. >> >> The speed improvements have been made possible by optimizing many of the >> layers which compose PyPy. In particular, we improved: the Garbage >> Collector, >> the JIT warmup time, the optimizations performed by the JIT, the quality of >> the generated machine code and the implementation of our Python interpreter. >> >> .. _`pypy 1.5 and cpython 2.6.2`: http://speed.pypy.org >> >> >> Highlights >> ========== >> >> * Numerous performance improvements, overall giving considerable speedups: >> >> - better GC behavior when dealing with very large objects and arrays >> >> - **fast ctypes:** now calls to ctypes functions are seen and optimized >> by the JIT, and they are up to 60 times faster than PyPy 1.5 and 10 times >> faster than CPython >> >> - improved generators(1): simple generators now are inlined into the caller >> loop, making performance up to 3.5 times faster than PyPy 1.5. >> >> - improved generators(2): thanks to other optimizations, even generators >> that are not inlined are between 10% and 20% faster than PyPy 1.5. >> >> - faster warmup time for the JIT >> >> - JIT support for single floats (e.g., for ``array('f')``) >> >> - optimized dictionaries: the internal representation of dictionaries is >> now >> dynamically selected depending on the type of stored objects, resulting >> in >> faster code and smaller memory footprint. For example, dictionaries >> whose >> keys are all strings, or all integers. Other dictionaries are also >> smaller >> due to bugfixes. >> >> * JitViewer: this is the first official release which includes the JitViewer, >> a web-based tool which helps you to see which parts of your Python code >> have >> been compiled by the JIT, down until the assembler. The `jitviewer`_ 0.1 >> has >> already been release and works well with PyPy 1.6. >> >> * The CPython extension module API has been improved and now supports many >> more extensions. For information on which one are supported, please refer >> to >> our `compatibility wiki`_. >> >> * Multibyte encoding support: this was of of the last areas in which we were >> still behind CPython, but now we fully support them. >> >> * Preliminary support for NumPy: this release includes a preview of a very >> fast NumPy module integrated with the PyPy JIT. Unfortunately, this does >> not mean that you can expect to take an existing NumPy program and run it >> on >> PyPy, because the module is still unfinished and supports only some of the >> numpy API. However, barring some details, what works should be >> blazingly fast :-) >> >> * Bugfixes: since the 1.5 release we fixed 53 bugs in our `bug tracker`_, not >> counting the numerous bugs that were found and reported through other >> channels than the bug tracker. >> >> Cheers, >> >> Hakan Ardo, Carl Friedrich Bolz, Laura Creighton, Antonio Cuni, >> Maciej Fijalkowski, Amaury Forgeot d'Arc, Alex Gaynor, >> Armin Rigo and the PyPy team >> >> .. _`jitviewer`: >> http://morepypy.blogspot.com/2011/08/visualization-of-jitted-code.html >> .. _`bug tracker`: https://bugs.pypy.org >> .. _`compatibility wiki`: https://bitbucket.org/pypy/compatibility/wiki/Home >> _______________________________________________ >> pypy-dev mailing list >> pypy-dev@python.org >> http://mail.python.org/mailman/listinfo/pypy-dev >> > _______________________________________________ pypy-dev mailing list pypy-dev@python.org http://mail.python.org/mailman/listinfo/pypy-dev
