Optimizing dense linear algebra algorithms on heterogeneous machines

• Tipo: Capítulo ou Parte de Livro
• Data: 2006

Autores

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

Livro

Título: Algorithms and Tools for Parallel Computing on Heterogeneous Clusters

Páginas: 17-31

ISBN: 1-60021-049-X

Editora: Nova Science Publishers

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Físicas > Matemática > Algoritmos

Outras Informações

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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica