[ACM Press the 19th ACM International Symposium - Chicago, Illinois (2010.06.21-2010.06.25)] Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing - HPDC '10 - GPU-based parallel householder bidiagonalization

✍ Scribed by Liu, Fangbin; Seinstra, Frank J.

• Book ID: 111900607
• Publisher: ACM Press
• Year: 2010
• Tongue: English
• Weight: 124 KB
• Volume: 0
• Category: Article
• ISBN: 160558942X
• DOI: 10.1145/1851476.1851512

✦ Synopsis

In this paper, we discuss the GPU-based implementation and optimization of Householder bidiagonalization, a matrix factorization method which is an integral part of full Singular Value Decomposition (SVD) -an important algorithm for many problems in the research domain of Multimedia Content Analysis (MMCA). On cluster computers, complex adaptive run-time techniques often must be implemented to overcome the growing negative performance impact of load imbalances and to ensure reasonable speedup. We show that the nature of the many-core platform can avoid the necessity of applying such complex run-time parallelization techniques in software while achieving a performance of 64 gigaflops/s on a single-GPU GTX 295 in double precision, 82% of the theoretical peak performance.