OverSketch: Approximate Matrix Multiplication for the Cloud
November 06, 2018 Β· Declared Dead Β· π 2018 IEEE International Conference on Big Data (Big Data)
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Authors
Vipul Gupta, Shusen Wang, Thomas Courtade, Kannan Ramchandran
arXiv ID
1811.02653
Category
cs.DC: Distributed Computing
Cross-listed
cs.IT
Citations
52
Venue
2018 IEEE International Conference on Big Data (Big Data)
Last Checked
5 months ago
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.
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