2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-6.1
Paper Title APPROXIMATE WEIGHTED CR CODED MATRIX MULTIPLICATION
Authors Neophytos Charalambides, University of Michigan, United States; Mert Pilanci, Stanford University, United States; Alfred Hero, University of Michigan, United States
SessionSPTM-6: Sampling, Multirate Signal Processing and Digital Signal Processing 2
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Signal Processing Theory and Methods: [SMDSP] Sampling, Multirate Signal Processing and Digital Signal Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract One of the most common operations in signal processing is matrix multiplication. However, it presents a major computational bottleneck when the matrix dimension is high, as can occur for large data size or feature dimension. Two different approaches to overcoming this bottleneck are: 1) low rank approximation of the matrix product; and 2) distributed computation. We propose a scheme that combines these two approaches. To enable distributed low rank approximation, we generalize the approximate matrix CR-multiplication to accommodate weighted block sampling, and we introduce a weighted coded matrix multiplication method. This results in novel approximate weighted CR coded matrix multiplication schemes, which achieve improved performance for distributed matrix multiplication and are robust to stragglers.