Proc. ACM Meas. Anal. Comput. Syst., Vol. 3, No. 3, Article 58. December 2019.
Ankur Mallick, Malhar Chaudhari*, Ganesh Palanikumar‡, Utsav Sheth†, Gauri Joshi
Carnegie Mellon University
* Oracle Corporation
† Automation Anywhere
‡ Apple Inc.
Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrix-matrix multiplication operations that need to be parallelized across multiple nodes. The presence of straggling nodes – computing nodes that unpredictably slowdown or fail – is a major bottleneck in such distributed computations. Ideal load balancing strategies that dynamically allocate more tasks to faster nodes require knowledge or monitoring of node speeds as well as the ability to quickly move data. Recently proposed fixed-rate erasure coding strategies can handle unpredictable node slowdown, but they ignore partial work done by straggling nodes thus resulting in a lot of redundant computation.We propose a rateless fountain coding strategy that achieves the best of both worlds – we prove that its latency is asymptotically equal to ideal load balancing, and it performs asymptotically zero redundant computations. Our idea is to create linear combinations of them rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more thanm row-vector products are collectively finished by the nodes. We conduct experiments in three computing environments: local parallel computing, Amazon EC2, and Amazon Lambda, which show that rateless coding gives as much as 3× speed-up over uncoded schemes.
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