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

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Overview of QUIC-SVD [Holmes et al. 2008] A V Repeat, until the estimated L2 error is below a threshold. The basis set now provides a good low-rank approximation. 16
Overview of QUIC-SVD • Monte Carlo Error Estimation: • Input: any subset of rows A s , the basis V, δ ∈ [0,1] • Output: estimated , s.t. with probability at least 1 − δ, the L2 error: • Termination Criteria: 17
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 13 and 14: Overview of QUIC-SVD [Holmes et al.
Page 15: Overview of QUIC-SVD [Holmes et al.
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

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