Campus Units
Electrical and Computer Engineering, Mathematics
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
5-2020
Journal or Book Title
IEEE Signal Processing Magazine
Volume
37
Issue
3
First Page
136
Last Page
145
DOI
10.1109/MSP.2020.2974149
Abstract
The current BigData era routinely requires the processing of large scale data on massive distributed computing clusters. Such large scale clusters often suffer from the problem of "stragglers", which are defined as slow or failed nodes. The overall speed of a computational job on these clusters is typically dominated by stragglers in the absence of a sophisticated assignment of tasks to the worker nodes. In recent years, approaches based on coding theory (referred to as "coded computation") have been effectively used for straggler mitigation. Coded computation offers significant benefits for specific classes of problems such as distributed matrix computations (which play a crucial role in several parts of the machine learning pipeline). The essential idea is to create redundant tasks so that the desired result can be recovered as long as a certain number of worker nodes complete their tasks. In this survey article, we overview recent developments in the field of coding for straggler-resilient distributed matrix computations.
Rights
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Copyright Owner
IEEE
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Ramamoorthy, Aditya; Das, Anindya Bijoy; and Tang, Li, "Straggler-Resistant Distributed Matrix Computation via Coding Theory: Removing a Bottleneck in Large-Scale Data Processing" (2020). Electrical and Computer Engineering Publications. 239.
https://lib.dr.iastate.edu/ece_pubs/239
Comments
This is a manuscript of an article published as Ramamoorthy, Aditya, Anindya Bijoy Das, and Li Tang. "Straggler-Resistant Distributed Matrix Computation via Coding Theory: Removing a Bottleneck in Large-Scale Data Processing." IEEE Signal Processing Magazine 37, no. 3 (2020): 136-145. DOI: 10.1109/MSP.2020.2974149.Posted with permission.