by Antonio Cuni and Armin Rigo
The PyPy project has recently gathered a lot of attention for its
progress in speeding up the Python language -- it is the fastest
Python interpreter, and the most compatible and most stable
'alternative´ one. No longer merely a research project, PyPy
is now suitable for production use. We are working on improvements
on calling into C libraries and generally integrating with
the existing Python extensions ecosystem.
We will give an overview on how the tracing Just-in-Time compiler
works in PyPy. From there, we will then focus on what the PyPy
project has achieved, particularly in the past two years:
We want to reserve time for discussing potential future work like SWIG
and/or Cython compatibility and other areas brought up by the audience.
There are many interesting details that can be explored further;
we will focus on the points the audience is most interested in.
For more info:
 Eurostars Eureka is our funding source since 2009. It is a
cross-European funding collaboration that targets small firms
which produce research.
by Mark Shannon
CPython can be made faster by implementing the sort of
optimizations used in the PyPy VM, and in my HotPy VM.
All the necessary changes can be made without modifying the language or the API.
The CPython VM can be modified to support optimizations by adding
an effective garbage collector and by separating the
virtual-machine state from the real-machine state (like Stackless).
Optimizations can be implemented incrementally.
Since almost all of the optimizations are implemented in the interpreter,
all hardware platforms can benefit.
JIT compiler(s) can then be added for common platforms (intel, ARM, etc.).
For more information see http://hotpy.blogspot.com/
20th–26th June 2011