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Author(s): Jorge Barbosa
João Tavares
A. J. Padilha
Title: Optimizing dense linear algebra algorithms on heterogeneous machines
Issue Date: 2006
Abstract: 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.
Subject: Algoritmos, Engenharia electrotécnica, electrónica e informática
Algorithms, Electrical engineering, Electronic engineering, Information engineering
Scientific areas: Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Source: Algorithms and Tools for Parallel Computing on Heterogeneous Clusters
Document Type: Capítulo ou Parte de Livro
Rights: restrictedAccess
Appears in Collections:FEUP - Capítulo ou Parte de Livro

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