@inproceedings{73cb2ef593d14523a113fe40e2c7b234,
title = "Distributed Matrix Computations with Low-weight Encodings",
abstract = "Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such stragglers is to incorporate MDS (maximum distance separable) codes into the framework; this can achieve resilience against an optimal number of stragglers. However, these codes assign dense linear combinations of submatrices to the workers which increase the number of non-zero entries in the encoded matrices, and adversely affect the worker computation time. In this work, we develop a straggler-optimal distributed matrix computation approach where the assigned encoded submatrices are linear combinations of a small number of submatrices so that it is well suited for sparse input matrices. Numerical experiments conducted in Amazon Web Services (AWS) demonstrate up to 30% reduction in worker computation time and 100 times faster encoding compared to several recent methods.",
keywords = "Condition Number, Distributed computing, MDS codes, Sparsity, Straggler",
author = "Das, {Anindya Bijoy} and Aditya Ramamoorthy and Love, {David J.} and Brinton, {Christopher G.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Symposium on Information Theory, ISIT 2023 ; Conference date: 25-06-2023 Through 30-06-2023",
year = "2023",
doi = "10.1109/ISIT54713.2023.10206445",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1979--1984",
booktitle = "2023 IEEE International Symposium on Information Theory, ISIT 2023",
address = "United States",
}