Technical Program

Paper Detail

Paper IDD.3.4
Paper Title Straggler-free Coding for Concurrent Matrix Multiplications
Authors Pedro Soto, Jun Li, Florida International 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 Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to compute the multiplication of large matrices, it is common practice to distribute the computation into multiple tasks running on different nodes. In order to tolerate potential stragglers among such nodes, various coding schemes have been proposed which add additional coded tasks. However, most existing coding schemes for the matrix multiplication are constructed for only one matrix multiplication, while it is common to compute multiple matrix multiplications concurrently in large-scale distributed computing workloads. In this paper, we propose a novel coding framework where the results of multiple multiplications can be obtained within one job concurrently. Compared with running the multiplications separately with multiple jobs, our work demonstrates that the same number of stragglers can be tolerated with much fewer tasks.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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