psom:MainPage

From NITRC Wiki

The pipeline system for Octave and Matlab (PSOM) is a lightweight library to manage complex multi-stage data processing. A pipeline is a collection of jobs, i.e. Matlab or Octave codes with a well identified set of options that are using files for inputs and outputs. To use PSOM, the only requirement is to generate a description of a pipeline in the form of a simple Matlab/Octave structure. PSOM then automatically offers the following services

• Run jobs in parallel using multiple CPUs or within a distributed computing environment.
• Generate log files and keep track of the pipeline execution. These logs are detailed enough to fully reproduce the analysis.
• Handle job failures : successful completion of jobs is checked and failed jobs can be restarted.
• Handle updates of the pipeline : change options or add jobs and let PSOM figure out what to reprocess !

PSOM is an opensource project distributed under an MIT opensource license. There is a paper in Frontiers in Neuroinformatics that provides an overview of PSOM features and implementation. It is currently in a beta-testing stage and has been tested under Linux, Windows and Mac OSX (see the test page). To install PSOM, just extract the archive in a folder and add that folder to your matlab or octave search path. You're done ! You may have to adapt the configuration to your local production environment. To use PSOM, you can have a look at the code of `psom_demo_pipeline`, or read the tutorial.

PSOM is maintained by Pierre Bellec, "Unité de Neuroimagerie Fonctionnelle" (UNF), "Centre de Recherche de l'Institut de Gériatrie de Montréal" (CRIUGM), "Département d'Informatique et de Recherche Opérationnelle" (DIRO), Université de Montréal, 2010-2011. The project was started by Pierre Bellec in the lab of Alan Evans at the McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Canada, 2008-10. Core ideas have been inspired by the Poor Man's Pipeline (PMP) project developed by Jason Lerch, which was itself based on RPPL by Alberto Jimenez and Alex Zijdenbos.