Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9413800 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Randomized numerical linear algebra, approximation algorithms, coded computing, coding theory | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neophytos Charalambides | 1 | 0 | 0.34 |
| Mert Pilanci | 2 | 95 | 17.13 |
| Alfred O. Hero III | 3 | 2600 | 301.12 |