Just a quick note to tell you that we are progressing on the JIT front. Here are the running times of the richards benchmark on my laptop:
- 8.18 seconds with CPython 2.5.2;
- 2.61 seconds with
pypy-c-jit(3x faster than CPython);
- 1.04 seconds if you ignore the time spent making assembler (8x faster than CPython);
- 1.59 seconds on Psyco, for reference (5x faster that CPython).
Yes, as this table shows, we are spending 1.57 seconds in the JIT support code. That's too much -- even ridiculously so -- for anything but a long-running process. We are working on that :-)
If you want to build your own
pypy-c-jit (for x86-32 only for now):
- you need a Subversion checkout of trunk;
- as usual, wait a long time (and be sure you have more than 1GB of RAM).
pypy-c-jit spews a lot of debugging output and
there are a few known
examples where it crashes. As we like to repeat, however, it's a complete JIT:
apart from the crashes (the bugs are probably in the JIT support code), it supports the whole Python language from the start -- in the sense of doing correct things. Future work include
Python-specific improvements by e.g. tweaking the data structures used to store Python objects so that they are more JIT-friendly.
EDIT: Oh yes, fijal reminds me that CPython 2.6 is 30% faster than CPython 2.5 on this benchmark (which is mostly my "fault", as I extracted a small part of PyPy and submitted it as a patch to CPython that works particularly well for examples like richards). It does not fundamentally change the fact that we are way faster though.