By Topic

Performance Modeling of MPI Applications Using Model Selection Techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Diego Rodriguez Martinez ; Dept. of Electron. & Comput. Sci., Univ. of Santiago de Compostela, Santiago de Compostela, Spain ; Jose Carlos Cabaleiro ; Tomas Fernandez Pena ; Francisco Fernandez Rivera
more authors

A new method for obtaining models of the performance of parallel applications based on statistical analysis is presented in this paper. This method is based on the Akaike's information criterion (AIC) that provides an objective mechanism to rank different models by means of an experimental data fit. The input of the modeling process is a set of variables and parameters that can a priori influence the performance of the application. This set can be provided by the user. Using this information, the method automatically generates a set of candidate models. These models are fit to the experimental data and the AIC score of each model is calculated. The model with the best AIC score is selected as the best model. Also, using the AIC scores of all candidate models, useful statistical information is provided to help the user to evaluate the quality of the selected model, as well as indications of how to interactively improve this modeling process. As a first case of study, statistical models obtained for different implementations of the broadcast collective communication in Open MPI are shown. These models are very accurate, exceeding its adjustment to theoretical approaches based on the LogGP model. Finally, the NAS Parallel Benchmark is also characterized using this new method with good results in terms of accuracy.

Published in:

2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing

Date of Conference:

17-19 Feb. 2010