The news reporters obtained a quote from the research from ENSEEIHT, "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."
According to the news reporters, the research concluded: "Experimental results show that an implementation of the proposed approach achieves higher performance and better scalability than existing equivalent software."
For more information on this research see: Fine-grained Multithreading For The Multifrontal Qr Factorization Of Sparse Matrices.
Our news correspondents report that additional information may be obtained by contacting A. Buttari, ENSEEIHT, CNRS IRIT, F-31071
Keywords for this news article include:
Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2013, NewsRx LLC
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