Coded Distributed Matrix Multiplication – A Survey and Some Recent Results
We consider the problem of multiplying two large matrices using a distributed computing model. Specifically, we consider a master-worker model where a master node splits the computation into many small tasks and worker nodes perform these small tasks in a distributed fashion. One of the main bottlenecks in the performance of such distributed systems is the presence of stragglers (worker nodes which are slow). In this talk, we will review some recent results on the use of coding theory to provide resilience to stragglers, thereby speeding up the task of matrix multiplication. We will point out connections to fault-tolerant computing and numerical analysis.
The talk will not assume any prior knowledge of coding theory and will be accessible to most graduate students in electrical engineering, computer engineering and computer science.
Professor, Texas A&M University on March 26, 2021 at 10:00 AM in Zoom Webinar
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Krishna Narayanan is a Professor in the ECE department at Texas A&M University. His research interests are broadly in coding theory, information theory, and signal processing with applications to wireless networks, data storage and data science. More specifically, his current research interests are in massive multiple access, coding for distributed computing, exploring connections between sparse signal recovery and coding theory, and analyzing data defined on graphs. He is the recipient of the IEEE Comsoc best paper in data storage award in 2006 and 2019. He served as an associate editor for coding techniques for the IEEE Transactions on Information Theory from 2016–2019. He served as the area editor (and as an editor) for the coding theory and applications area of the IEEE Transactions on Communications from 2007 until 2011. In 2014, he received the Professional Progress in Engineering award given to an outstanding alumnus of Iowa State University. He was elected a Fellow of the IEEE for contributions to coding for wireless communications and data storage. On the teaching side, he is passionate about technology-enabled teaching and innovative pedagogical approaches and he was the recipient of the 2018 Association of Former Students university level distinguished teaching award.
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