Overview of QUIC-SVD - UMass Graphics Lab - University of ...

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Partitioned QUIC-SVD • The advantage of the GPU is only obvious for largescale problems, e.g. matrices larger than 10,000 2 . • For dense matrices, this will soon become an out-ofcore problem. • We modified our algorithm to use a matrix partitioning scheme, so that each partition can fit in GPU memory. 26
… … … … Partitioned QUIC-SVD • Partition the matrix into fixed-size blocks A 1 A 2 A s 27
Page 1 and 2: A GPU-based Approximate SVD Algorit
Page 3 and 4: Introduction • Approximate SVD
Page 5 and 6: Introduction • Sample-based Appro
Page 7 and 8: Our Goal • Map QUIC-SVD onto the
Page 9 and 10: Our Work • A CUDA implementation
Page 11 and 12: Overview of QUIC-SVD [Holmes et al.
Page 17 and 18: Overview of QUIC-SVD • Monte Carl
Page 19 and 20: GPU Implementation of QUIC-SVD •
Page 21 and 22: Parallelize Gram Schmidt Orthogonal
Page 23 and 24: Parallelize Gram Schmidt Orthogonal
Page 25: Extract the Final Result • Input:
Page 29 and 30: Partitioned QUIC-SVD 29
Page 31 and 32: Implementation Details • Main imp
Page 33 and 34: Performance • Comparisons: 1. Mat
Page 35 and 36: Performance - Speedup Factors 35
Page 37 and 38: Performance - CPU vs. GPU QUIC-SVD
Page 39 and 40: Integration with Manifold Alignment
Page 41 and 42: Future Work • Be able to process

Overview of QUIC-SVD - UMass Graphics Lab - University of ...

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