Abstract
In this paper, we propose a new parallel multifrontal algorithm for the orthogonal factorization of large sparse matrices on distributed-memory multiprocessors. We explore the use of block partitioning schemes in parallel sparse orthogonal factorization. Our block-oriented parallel algorithm for sparse orthogonal factorization achieves high performance by incurring strictly less communication overhead than the conventional nonblock algorithm, maintaining relatively balanced load distribution among processors, and accelerating the parallel numerical kernel via increased cache utilization. We analyze the performance of our parallel algorithm and present its arithmetic and communication complexities for regular grid problems. We report the experimental results of an implementation of our parallel algorithm on an Intel iPSC/860 machine. Through our theoretical analysis and experimental results, we demonstrate that our new block-oriented algorithm outperforms the conventional nonblock algorithm impressively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
