PyPy
==================

PyPy is both:

  - a Python interpreter written in Python

  - a framework for writing dynamic languages

Today we will focus on the latter.


Agenda
======

* quick overview & motivations

* the translation framework

* JIT generator

* (demo)


PyPy status update (1)
======================

- JVM backend completed, pypy-jvm

- some new GCs, much faster than ever

- ctypes for PyPy

- JIT refactoring, needed to make the JIT production-ready

- improved .NET integration for pypy-cli

- new blog: http://morepypy.blogspot.com


PyPy status update (2)
======================

- various performance improvements

- slighly slower than CPython on pystone (10-20%)

- but faster on richards (20-24%)

- less than 2x slower on other benchmarks


Interpreters
==================

...are good to implement dynamic languages:

* Easy to write

* Portable

* Flexible and easy to evolve, if written in high-level language
  (without low-level details)

Folk Wisdom
====================================================

...about interpreters for Dynamic Languages:

* There are unavoidable tradeoffs between flexibility, maintainability,
  and speed

* Fast, Maintainable, Flexible -- pick one

What this means in Practice
===========================

Current popular open source dynamic language implementations:

* are relatively slow

* are not very flexible

* are harder to maintain than we would like them to be

Not very flexible
=================

- Low-level decisions permeate the entire code base.
- Not ideal to experiment - cannot simply plug-in a new garbage collector,
  memory model, or threading model
- Early decisions come back to haunt you.

Hard to maintain
================

because they are traditionally written in low-level languages:

- the community generates experts in the dynamic language but
  requires experts in C or C++ for its own maintenance
- every time a new VM is needed, the language's community forks
  (CPython - Jython - IronPython)


The PyPy Project
==================

We built enough infrastructure such that:

* speed is regained

* features requiring low-level manipulations are (re-)added as *aspects*

* interpreters are kept simple and uncluttered

Targets as different as C and the industry OO VMs (JVM, CLR) are supported.

A special aspect: **Generating JIT compilers**


PyPy as a project
===================

- We operate both as an open source with production usage aspirations and
  research project.

- We focus on the whole system.

- We want the tool-chain itself to be as simple as possible
  (but not simpler).

- Some of what we do is relatively straight-forward, some is challenging
  (generating dynamic compilers!).



PyPy Approach
=============================

.. image:: overview1.png
   :align: center
   :scale: 45


Going from interpreters to VMs
==============================

In PyPy interpreters are written in RPython:

* A subset of Python amenable to static analysis

* Still fully garbage collected

* Rich built-in types

RPython is still close to Python.