Optimizing dense linear algebra algorithms on heterogeneous machines

• Type: Chapter or Part of a Book
• Date: 2006

Authors

Jorge Barbosa (Author) FEUP; João Tavares (Author) FEUP; A. J. Padilha (Author) FEUP

Book

Title: Algorithms and Tools for Parallel Computing on Heterogeneous Clusters

Pages: 17-31

ISBN: 1-60021-049-X

Publisher: Nova Science Publishers

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Physical sciences > Mathematics > Algorithms

Other information

Abstract (EN): This paper addresses the execution of inherently sequential linear algebra algorithms namely LU factorization, tridiagonal reduction and the symmetric QR factorization algorithm used for eigenvector computation, which are significant building blocks for applications in our target image processing and analysis domain. These algorithms present additional difficulties to optimize the processing time due to the fact that the computational load for data matrix columns increases with their index, requiring a fine tuned load assignment and distribution. We present an efficient methodology to determine the optimal number of processors to be used in a computation, as well as a new static load distribution strategy that achieves better results than other algorithms developed for the same purpose.

Language: English

Type (Professor's evaluation): Scientific