.. include:: <s5defs.txt>

=================================================
PyPy Introduction
=================================================

:Authors: PyPy Team
:Date: 5th October 2006
:Location: AB Strakt, Gothenburg

What it is?
===================

- Python implementation
- Open source project (MIT license)
- An EU cofunded research project (~2 years)
- A compiler tool chain

Goals (i)
======================

An Architecture for flexibility

- some decisions are invasive
  and fixed in CPython (e.g.
  refcounting, the GIL)

- some extensions are possible
  but hard to maintain:

  * stackless
  * psyco

Goals (ii)
=======================

An architecture for speed

- generate a psyco-like JIT

(right now without a JIT
we can be 2.7x-6x slower than
CPython, depending on features
etc, we can still improve on
this too)

Goals (iii)
=======================

Multiple targets

- POSIX/C (like CPython)
- .NET (like IronPython)
- JavaScript

How
=====================

- Python interpreter written
  in Python, low-level details
  left out (30000 LOC)
- a subset static enough
  to allow analysis (RPython)
- translated to low-level targets
  filling in the details (C, .NET,
  ...)

The Basic Architecture
=======================


.. image:: arch-pypy-basic.png
   :align: center

Status
==========================

- conformant interpreter
  (95% of core tests)
- translates to C, LLVM
  (since August last year)
- 0.9 was the last release
- PyPy translated to .NET
  (no .NET gluing, much work
  left)

0.9
=========================

- more speed improvements (3x 0.8, without JIT)
- our own GCs
- stackless features: tasklets,
  channels, coroutine, cloning of coroutines,
  pickling of tasklets
- start of logic, search and constraint
  programming features
- ext-compiler

Ext-Compiler
=========================

- we can glue with C using
  a ctypes-like interface from RPython
  (rctypes)
- extension modules for both PyPy and
  CPython
- work in progress

PyPy/Translation overview
=========================

.. image:: arch-translation.png
   :align: center
   :width: 500
   :height: 500

Abstract Interpretation
========================

- bytecode interpreter dispatches to Flow Object Space
- Flow Object Space implements abstract operations
- produces flow graphs as a side effect
- starts from "live" byte code NOT source code

Type Inference 
===============

- performs forward propagating type inference
- is used to infer the types in flow graphs
- needs types of the entry point function's arguments
- assumes that the used types are static
- goes from very special to more general values

RTyping
===========================

- annotated flow graphs are specialized for language families
- choose runtime representations: express these with
  low-level type systems
- lltypesystem (for C like languages): C, LLVM
- ootypesystem (for OO languages): .NET CLI, (Javascript...)
- result is specialized flow graphs: operations at target level

Translation Aspects
====================

- implementation decisions (GC, threading, CC) at translation time 
- most other language implementations do a "fixed" decision 
- translation aspects are weaved into the produced code
- independent from language semantics (python interpreter)


A Special Aspect: Just-in-time Compilation
===========================================

- transform interpreters into compilers
  (and just-in-time compilers)
- work in progress

JIT Diagram
============

.. image:: arch-jit-gen.png
   :align: center
   :width: 500
   :height: 500

Website
===========

http://codespeak.net/pypy


.. |bullet| unicode:: U+02022
.. footer:: PyPy |bullet| AB Strakt |bullet| 5th October 2006