Straggler-Resistant Distributed Matrix Computation via Coding Theory: Removing a Bottleneck in Large-Scale Data Processing

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Das, Anindya Bijoy
Tang, Li
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Electrical and Computer Engineering

The Department of Electrical and Computer Engineering (ECpE) contains two focuses. The focus on Electrical Engineering teaches students in the fields of control systems, electromagnetics and non-destructive evaluation, microelectronics, electric power & energy systems, and the like. The Computer Engineering focus teaches in the fields of software systems, embedded systems, networking, information security, computer architecture, etc.

The Department of Electrical Engineering was formed in 1909 from the division of the Department of Physics and Electrical Engineering. In 1985 its name changed to Department of Electrical Engineering and Computer Engineering. In 1995 it became the Department of Electrical and Computer Engineering.

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  • Department of Electrical Engineering (1909-1985)
  • Department of Electrical Engineering and Computer Engineering (1985-1995)

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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.


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.

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Wed Jan 01 00:00:00 UTC 2020