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

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Performance • Test environment: • CPU: Intel Core i7 2.66 GHz (8 HTs), 6 GB memory • GPU: NVIDIA GTX 480 GPU • Test data: • Given a rank k and matrix size n, we generate a n × k left matrix and k × n right matrix, both filled with random numbers; the product of the two gives the test matrix. • Result accuracy: • Both CPU and GPU versions use double floats; termination error ε = 10 −12 , Monte Carlo estimation δ = 10 −12 . 32
Performance • Comparisons: 1. Matlab’s svds; 2. CPU-based QUIC-SVD (optimized, multi-threaded, implemented using Intel Math Kernel Library); 3. GPU-based QUIC-SVD. 4. Tygert SVD (approximate SVD algorithm, implemented in Matlab, based on random projection); 33
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 and 26: Extract the Final Result • Input:
Page 27 and 28: … … … … Partitioned QUIC-SV
Page 29 and 30: Partitioned QUIC-SVD 29
Page 31: Implementation Details • Main imp
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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