2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | D.3.5 | ||
| Paper Title | Factored LT and Factored Raptor Codes for Large-Scale Distributed Matrix Multiplication | ||
| Authors | Asit Kumar Pradhan, Anoosheh Heidarzadeh, Krishna R. Narayanan, Texas A&M University, United States | ||
| Session | D.3: Distributed Matrix Multiplication | ||
| Presentation | Lecture | ||
| Track | Coded and Distributed Computation | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We propose two coding schemes for distributed matrix multiplication in the presence of stragglers. These coding schemes are adaptations of LT codes and Raptor codes to distributed matrix multiplication and are termed Factored LT (FLT) codes and Factored Raptor (FR) codes. Empirically, we show that FLT codes have a near-optimal recovery threshold when the number of worker nodes is very large, and that FR codes have an excellent recovery threshold while the number of worker nodes is moderately large. FLT and FR codes have better recovery thresholds when compared to Product codes and they are expected to have better numerical stability when compared to Polynomial codes, while they can also be decoded with a low-complexity decoding algorithm. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
