TY - GEN
T1 - Automatic parallelization with pMapper
AU - Travinin, Nadya
AU - Hoffmann, Henry
AU - Bond, Robert
AU - Chan, Hector
AU - Kepner, Jeremy
AU - Wong, Edmund
PY - 2005/12/1
Y1 - 2005/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=50149095634&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50149095634&partnerID=8YFLogxK
U2 - 10.1109/CLUSTR.2005.347017
DO - 10.1109/CLUSTR.2005.347017
M3 - Conference contribution
AN - SCOPUS:50149095634
SN - 0780394852
SN - 9780780394858
T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC
BT - 2005 IEEE International Conference on Cluster Computing, CLUSTER
T2 - 2005 IEEE International Conference on Cluster Computing, CLUSTER
Y2 - 27 September 2005 through 30 September 2005
ER -