Cart (Loading....) | Create Account
Close category search window
 

Load Balancing in Mesh-like Computations using Prediction Binary Trees

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
$31 $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)
Cosenza, B. ; Dipt. di Inf. ed Applicazioni "R.M. Capocelli", Univ. degli Studi di Salerno, Salerno, Italy ; Cordasco, G. ; De Chiara, R. ; Erra, U.
more authors

We present a load-balancing technique that exploits the temporal coherence, among successive computation phases, in mesh-like computations to be mapped on a cluster of processors. Our method partitions the computation in balanced tasks and distributes them to independent processors through the prediction binary tree (PBT). At each new phase, current PBT is updated by using previous phase computing time (for each task) as (next phase) cost estimate. The PBT is designed so that it balances the load across the tasks as well as reduce {em dependency} among processors for higher performances. Reducing dependency is obtained by using rectangular tiles of the mesh, of almost-square shape (i.e. one dimension is at most twice the other). By reducing dependency, one can reduce inter-processors communication or exploit local dependencies among tasks (such as data locality).Our strategy has been assessed on a significant problem, parallel ray tracing. Our implementation shows a good scalability, and improves over coherence-oblivious implementations. We report different measurements showing that granularity of tasks is a key point for the performances of our decomposition/mapping strategy.

Published in:

Parallel and Distributed Computing, 2008. ISPDC '08. International Symposium on

Date of Conference:

1-5 July 2008

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.