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

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Introduction • Singular Value Decomposition (SVD) • A: m × n matrix (m ≥ n) • U, V: orthogonal matrices • Σ: diagonal matrix • Exact SVD can be computed in O(mn 2 ) time. 2
Introduction • Approximate SVD • A: m × n matrix, whose intrinsic dimensionality is far less than n • Usually sufficient to compute the k (≪ n) largest singular values and corresponding vectors. • Approximate SVD can be solved in O(mnk) time ( compared to O(mn 2 ) for full svd). 3
Page 1: A GPU-based Approximate SVD Algorit
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 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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