Jeremy Cowles, Eric Heien, Adam Kornafeld, Yusuke Takata, Kenichi Hagihara, Nicolás Alvarez

Overview  PyMW & Summer of Code  Recent Enhancements in 0.3  PyBOINC: Distributable Python Interpreter  BOINC & PyMW at U.C. Berkeley  Future plans for PyMW 2

Google Summer of Code  Proposal accepted via Python Foundation  Worked with Eric Heien as project mentor  Goals:  Improve BOINC integration in PyMW  Reduce barriers to creating BOINC apps  Remove the Python requirement on the Client 3

PyMW v0.2 Limitations  Need to compile assimilator from C source  Manual project setup via script  Modules, libraries & data bundled by BOINC application, not by work unit  No direct support for BOINC API  Python installation is assumed on client 4

Enhancements in v0.3  Pure-Python assimilator  Abstract base class, easy to extend  Leverage existing Python code  Automated project setup  Validates project setup on every execution  Installs required daemons  Installs/upgrades science application 5

Enhancements in v0.3  Bundle arbitrary files with work units  Individual Python libraries  Domain-specific data  Customizable work unit generation  Number of target results, output size, etc  Native Python support for BOINC API 6

PyBOINC Python Interpreter  Collaboration with Nicolás Alvarez  Official Python interpreter (embedded)  BOINC compatible, distributable  Statically compiled for  Windows  Linux  Mac OS X 7

PyBOINC Features  Customizable standard library  Uses Python’s support for zipped modules  2.6 bundled with current release  Add or remove components as needed  3 rd party libraries  BOINC API module included by default  Compiled C modules & dynamic libraries 8

PyMW 0.3 Workflow  Download PyMW & PyBOINC  Write PyMW science application  Install BOINC server & create a project  Run the application:  $ myapp.py –d ~/projects/my_proj  Does it work with real applications? 9

PyMW & Berkeley 10 CS188: Introduction to Artificial Intelligence Pacman AI Tournament

 Large framework in Python w/data files  Student teams submit agents  Matches run nightly  Takes ~16 hours to run tournament (serial) 11

Using PyMW & BOINC  BOINC formulation:  Wrapped tourney framework with PyMW  One match per work unit  99% orthogonal to PyMW/BOINC  Students can volunteer compute time  PyMW multi-core for debugging  New tourney runs in about 1 hour 12

Future Plans for PyMW  Security & signing scripts  Support for BOINC Graphic API  Python 3.0 Support  Check-pointing support 13

Online Resources  PyMW  PyMW Documentation  PyBOINC 14