Adaptive Parallel Householder Bidiagonalization

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 reduct...

• Type: Conference
• Year: 2009
• Where: EUROPAR
• Authors: Fangbin Liu, Frank J. Seinstra
• Keywords: Compute Resources, Distributed And Parallel Computing, EUROPAR 2009, Householder Bidiagonalization, Matrix Factorization Method