Automatic parallelization with pMapper

Nadya Travinin, Henry Hoffmann, Robert Bond, Hector Chan, Jeremy Kepner, Edmund Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Algorithm implementation efficiency is key to delivering high-performance computing capabilities to demanding, high throughput signal and image processing applications and simulations. Significant progress has been made in optimization of serial programs, but many applications require parallel processing, which brings with it the difficult task of determining efficient mappings of algorithms. The pMapper infrastructure addresses the problem of performance optimization of multistage MATLAB® applications on parallel architectures. pMapper is an automatic performance tuning library written as a layer on top of pMatlab: Parallel Matlab Toolbox. While pMatlab abstracts the message-passing interface, the responsibility of mapping numerical arrays falls on the user. Choosing the best mapping for a set of numerical arrays is a nontrivial task that requires significant knowledge of programming languages, parallel computing, and processor architecture. pMapper automates the task of map generation. This abstract addresses the design details of pMapper and presents preliminary results.

Original languageEnglish (US)
Title of host publication2005 IEEE International Conference on Cluster Computing, CLUSTER
StatePublished - Dec 1 2005
Externally publishedYes
Event2005 IEEE International Conference on Cluster Computing, CLUSTER - Burlington, MA, United States
Duration: Sep 27 2005Sep 30 2005

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
ISSN (Print)1552-5244


Other2005 IEEE International Conference on Cluster Computing, CLUSTER
Country/TerritoryUnited States
CityBurlington, MA

ASJC Scopus subject areas

  • General Engineering


Dive into the research topics of 'Automatic parallelization with pMapper'. Together they form a unique fingerprint.

Cite this