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Fine-Grained Multithreading for the Multifrontal QR Factorization of Sparse Matrices

Buttari, Alfredo Fine-Grained Multithreading for the Multifrontal QR Factorization of Sparse Matrices. (2013) SIAM Journal on Scientific Computing, 35 (4). C323-C345. ISSN 1064-8275

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Official URL: http://dx.doi.org/10.1137/110846427

Abstract

The advent of multicore processors represents a disruptive event in the history of computer science as conventional parallel programming paradigms are proving incapable of fully exploiting their potential for concurrent computations. The need for different or new programming models clearly arises from recent studies which identify fine-granularity and dynamic execution as the keys to achieving high efficiency on multicore systems. This work presents an approach to the parallelization of the multifrontal method for the $QR$ factorization of sparse matrices specifically designed for multicore based systems. High efficiency is achieved through a fine-grained partitioning of data and a dynamic scheduling of computational tasks relying on a dataflow parallel programming model. Experimental results show that an implementation of the proposed approach achieves higher performance and better scalability than existing equivalent software.

Item Type:Article
Additional Information:Thanks to Society for Industrial and Applied Mathematics (SIAM) editor. The original publication is available at http://epubs.siam.org/doi/abs/10.1137/110846427
HAL Id:hal-01122471
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited By: IRIT IRIT
Deposited On:04 Mar 2015 07:36

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