Speeding up JSON encoding in PyPy
Expectations are high A lot of performance critical stuff in Python world is already written in a hand optimized C. Writing C (especially when you interface with CPython C API) is ugly and takes significant effort. This approach does not scale well when there is a lot of code to be written or when there is a very tight coupling between the part to be rewritten and the rest of the code. Still, peop
We need Software Transactional Memory
Hi all. Here is (an extract of) a short summary paper about my current position on Software Transactional Memory as a general tool in the implementation of Python or Python-like languages. Thanks to people on IRC for discussion on making this blog post better (lucian, Alex Gaynor, rguillebert, timonator, Da_Blitz). For the purpose of the present discussion, we are comparing Java with Python when i
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
Realtime image processing in Python
Image processing is notoriously a CPU intensive task. To do it in realtime, you need to implement your algorithm in a fast language, hence trying to do it in Python is foolish: Python is clearly not fast enough for this task. Is it? :-) Actually, it turns out that the PyPy JIT compiler produces code which is fast enough to do realtime video processing using two simple algorithms implemented by Håk
PyPy 1.5 Released: Catching Up
We're pleased to announce the 1.5 release of PyPy. This release updates PyPy with the features of CPython 2.7.1, including the standard library. Thus all the features of CPython 2.6 and CPython 2.7 are now supported. It also contains additional performance improvements. You can download it here: http://pypy.org/download.html What is PyPy? PyPy is a very compliant Python interpreter, almost a drop-
Using Tkinter and IDLE with PyPy
We are pleased to announce that Tkinter, the GUI library based on TCL/TK, now works with PyPy. Tkinter is composed of two parts: _tkinter, a module written in C which interfaces with the TCL world Tkinter, a pure Python package which wraps _tkinter to expose the pythonic API we are used to The PyPy version of _tkinter reuses the C code of as found in CPython and compile it through the PyPy C-API c
Tutorial: Writing an Interpreter with PyPy, Part 1
This is a guest blog post written by Andrew Brown, with help from the PyPy developers on the pypy-dev mailing list. This tutorial's master copy and supporting files live at https://bitbucket.org/brownan/pypy-tutorial/ When I first learned about the PyPy project, it took me a while to figure out exactly what it was about. For those that don't already know, it's two things: A set of tools for implem
PyPy faster than C on a carefully crafted example
Good day everyone. Recent round of optimizations, especially loop invariant code motion has been very good for small to medium examples. There is work ongoing to make them scale to larger ones, however there are few examples worth showing how well they perform. This one following example, besides getting benefits from loop invariants, also shows a difference between static and dynamic compilation.
A JIT for Regular Expression Matching
This is part 2 of a series, see Part 1 for an introduction. In this post I want to describe how the JIT generator of the PyPy project can be used to turn the elegant but not particularly fast regular expression matcher from the first part into a rather fast implementation. In addition, I will show some speed measurements against various regular expression implementations. Again, note the disclaime
Benchmarking twisted
To reproduce, run each benchmark with: benchname.py -n 12 -d 5 WARNING: running tcp-based benchmarks that open new connection for each request (web & accept) can exhaust number of some kernel structures, limit n or wait until next run if you see drops in request per second. The first obvious thing is that various benchmarks are more or less amenable to speedups by JIT compilation. Accept and tcp g
First results of the JIT
Hi all, 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 tabl
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CERN Sprint Report – Wrapping C++ Libraries