Static LU Decomposition on Heterogeneous Platforms

Authors:
Olivier Beaumont;Arnaud Legrand;Fabrice Rastello;Yves Robert
Affiliations:
LIP, UMR CNRS--ENS Lyon--INRIA 5668, Ecole Normale Supérieure de Lyon, France;LIP, UMR CNRS--ENS Lyon--INRIA 5668, Ecole Normale Supérieure de Lyon, France;LIP, UMR CNRS--ENS Lyon--INRIA 5668, Ecole Normale Supérieure de Lyon, France;LIP, UMR CNRS--ENS Lyon--INRIA 5668, Ecole Normale Supérieure de Lyon, France
Venue:
International Journal of High Performance Computing Applications
Year:
2001

Citing 8
Cited 3

Introduction to parallel computing: design and analysis of algorithms

Introduction to parallel computing: design and analysis of algorithms
A high-performance matrix-multiplication algorithm on a distributed-memory parallel computer, using overlapped communication

IBM Journal of Research and Development
Array decompositions for nonuniform computational environments

Journal of Parallel and Distributed Computing
ScaLAPACK user's guide

ScaLAPACK user's guide
The grid: blueprint for a new computing infrastructure

The grid: blueprint for a new computing infrastructure
High-performance schedulers

The grid
Heterogeneous Distribution of Computations While Solving Linear Algebra Problems on Networks of Heterogeneous Computers

HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Matrix-Matrix Multiplication on Heterogeneous Platforms

ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing

Adaptive parallel computing on heterogeneous networks with mpC

Parallel Computing
GREMLINS: a large sparse linear solver for grid environment

Parallel Computing
LU decomposition on cell broadband engine: an empirical study to exploit heterogeneous chip multiprocessors

NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the authors deal with algorithmic issues on heterogeneous platforms. They concentrate on dense linear algebra kernels, such as matrix multiplication or LU decomposition. Block-cyclic distribution techniques used in ScaLAPACK are no longer sufficient to balance the load among processors running at different speeds. The main result of this paper is to provide a static data distribution scheme that leads to an asymptotically perfect load balancing for LU decomposition, thereby providing solid foundations toward the design of a cluster-oriented version of ScaLAPACK.