.. include:: <s5defs.txt>

=========================================================
A generated JIT compiler for Python
=========================================================

:Author: Samuele Pedroni, Open End AB
:Date: December '09


Idea
==========

Derive a dynamic compiler from a sufficiently rich intermediate representation
of the interpreter while it is turned into a VM

- all features supported by construction

- semantics are more easily encoded as interpreting code

Tracing JIT compiler
=======================

Template: use the streamlined approach introduced by tracing JITs

- identify hot loops
- produce linear traces with stay-on-trace checks (guards), follow calls!
- compile the traces to assembler which run the loop

Details
=========

- Identifying loops: hints for the JIT runtime to identify the
  interpreter loop, and back edges of user loops

- Tracing: build encoding such that interpreter functions can be
  executed and their operation recorded in the traces

- Exploit when heap allocation can be forgone, mirror Python frames on
  the CPU stack

Example
=========


Status
=======

- Intel x86-32
- Small and some medium examples run
- Good results for algorithmic code, see benchmarks
- Reasonable heuristic when traces get too long

Benchmarks
============

.. image:: result.png
   :align: center

Work Left
==========

- Further work on memory footprint
- Correct and improved behavior in the presence of exceptions: work in progress
- Better results for recursion, asm to asm calls
- Bugs, correctness (sys.settrace for example)

Work Left (ii)
===============

- Machine code management, discard no longer valid traces
- Make the interpreter more JIT-friendly
- Interpreter-level algorithmic improvements
- Intel x86-64

Links
============

Status blog
http://morepypy.blogspot.com

Website
http://codespeak.net/pypy