PyPy

The PyPy project has recently gathered a lot of attention for its progress in speeding up the Python language -- it is the fastest, most compatible and most stable 'alternative' Python interpreter. No longer merely a research curiosity, PyPy is now suitable for production use.

The speed comes from a custom Just-in-Time compiler (JIT). It is the first Virtual Machine to have a JIT generated automatically from the interpreter of the language, for a complicated language like Python. It makes it correct and complete by construction. The JIT itself is a tracing JIT, roughly similar to TraceMonkey.

• most Python benchmarks run much faster than with CPython or Psyco
• the real-world PyPy compiler toolchain itself (200 KLocs) runs twice as fast
• supports x86 (32 or 64 bit), ARM (v7), and soon POWER64
• full compatibility with CPython (more than Jython/IronPython)
• ctypes, CFFI and C++ support to call C/C++ libraries from Python (fast)
• supports Stackless Python (in-progress)
• integrates existing CPython C extensions (slowly)

In this talk we will see examples of what PyPy is best at (pure Python code that runs for a while), what compatibility issues you may run into (very few), how to use CPython C extension modules (you can more or less, but it's slow), as well as dig a bit below the surface and use some tools to view the x86 machine code that was produced by the JIT.

I will end the talk with an overview of Software Transactional Memory (STM) and how it promizes to give a PyPy without the Global Interpreter Lock (GIL), i.e. able to run a single process using multiple cores.