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

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Related Work • OpenGL-based: rasterization, framebuffers, shaders • [Galoppo et al. 2005] • [Bondhugula et al. 2006] • CUDA-based: • Matrix factorizations [Volkov et al. 2008] • QR decomposition [Kerr et al. 2009] • SVD [Lahabar et al. 2009] • Commercial GPU linear algebra package [CULA 2010] 8
Our Work • A CUDA implementation of QUIC-SVD. • Up to 6~7 times speedup over an optimized CPU version. • A partitioned version that can process large, out-ofcore matrices on a single GPU • The largest we tested are 22,000 x 22,000 matrices (double precision FP). 9
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: Our Goal • Map QUIC-SVD onto the
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 and 26: Extract the Final Result • Input:
Page 27 and 28: … … … … Partitioned QUIC-SV
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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