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GPU-based parallel householder bidiagonalization
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing - HPDC '10
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.doi:10.1145/1851476.1851512 dblp:conf/hpdc/LiuS10 fatcat:s5wfunigwrf3jpmb4sy3ppuivu