Adaptive Parallel Householder Bidiagonalization

Authors:
Fangbin Liu;Frank J. Seinstra
Affiliations:
ISLA, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands 1098;Department of Computer Science, VU University, Amsterdam, The Netherlands 1081
Venue:
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Year:
2009

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Abstract

With the increasing use of large image and video archives and high-resolution multimedia data streams in many of today's research and application areas, there is a growing need for multimedia-oriented high-performance computing. As a consequence, a need for algorithms, methodologies, and tools that can serve as support in the (automatic) parallelization of multimedia applications is rapidly emerging. This paper discusses the parallelization of Householder bidiagonalization, a matrix factorization method which is an integral part of full Singular Value Decomposition (SVD) -- an important algorithm for many multimedia problems. Householder bidiagonalization is hard to parallelize efficiently because the total number of matrix elements taking part in the calculations reduces during runtime. To overcome the growing negative performance impact of load imbalances and overprovisioning of compute resources, we apply adaptive runtime techniques of periodic matrix remapping and process reduction for improved performance. Results show that our adaptive parallel execution approach provides a significant improvement in efficiency, even when applying a set of compute resources which is (initially) very large.