By Topic

Communication issues in parallel Conjugate Gradient method using a star-based network

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

1 Author(s)
Ismail, L. ; Fac. of Inf. Technol., UAE Univ., Al-Ain, United Arab Emirates

Conjugate Gradient (CG) method is an iterative linear solver which is used by many scientific and engineering applications to solve a linear system of algebraic equations. CG generates a heavy load of computation and therefore it slows the performance of the applications using it. Parallelizing CG is considered as a way to increase its performance. However, CG suffers from communication dependencies among its divisible loads. Most of the studies to parallelize CG concentrate on parallelizing its matrix-vector multiplication. In this paper, we answer the following questions: 1) what are the divisible loads in the CG, and 2) where is communication involved in the parallel CG. To answer 1), we highlight the different divisible data blocks in CG. To answer 2), we introduce a dependency graph among the different data blocks. We conduct experiments on a parallel CG implementation and evaluate communication cost.

Published in:

Computer Applications and Industrial Electronics (ICCAIE), 2010 International Conference on

Date of Conference:

5-8 Dec. 2010