OverSketch: Approximate Matrix Multiplication for the Cloud
arXiv:1811.02653 · doi:10.1109/BigData.2018.8622139
Abstract
We propose OverSketch, an approximate algorithm for distributed matrix multiplication in serverless computing. OverSketch leverages ideas from matrix sketching and high-performance computing to enable cost-efficient multiplication that is resilient to faults and straggling nodes pervasive in low-cost serverless architectures. We establish statistical guarantees on the accuracy of OverSketch and empirically validate our results by solving a large-scale linear program using interior-point methods and demonstrate a 34% reduction in compute time on AWS Lambda.
Published in Proc. IEEE Big Data 2018. Updated version provides details of distributed sketching and highlights other advantages of OverSketch