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Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Randomized Algorithm to Update Partial Singular Value Decomposition on a Hybrid CPU/GPU Cluster

SESSION: Sampling in Matrix Computations


EVENT TAG(S): Algorithms, Accelerators, Scientific Computing, Analytics

TIME: 10:30AM - 11:00AM

SESSION CHAIR(S): Shuaiwen Leon Song

AUTHOR(S):Ichitaro Yamazaki, Jakub Kurzak, Piotr Luszczek, Jack Dongarra



For data analysis, a partial singular value decomposition (SVD) of the sparse matrix representing the data is a powerful tool. However, computing the SVD of a large data can take a significant amount of time even on a large-scale computer. Hence, there is a growing demand for a novel algorithm that can efficiently process the massive data being generated from many modern applications. To address this challenge, in this paper, we study randomized algorithms to update the SVD as changes are made to the data. Our experimental results demonstrate that these randomized algorithms can obtain the desired accuracy of the SVD with a small number of data accesses, and compared to the state-of-the-art updating algorithm, they often require much lower computational and communication costs. Our performance results on a hybrid CPU/GPU cluster show that these randomized algorithms can obtain significant speedups over the state-of-the-art updating algorithm.

Chair/Author Details:

Shuaiwen Leon Song (Chair) - Pacific Northwest National Laboratory|

Ichitaro Yamazaki - University of Tennessee, Knoxville

Jakub Kurzak - University of Tennessee, Knoxville

Piotr Luszczek - University of Tennessee, Knoxville

Jack Dongarra - University of Tennessee, Knoxville

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society