OverSketch: Approximate Matrix Multiplication for the Cloud
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1; 2025: 2; 2026: 2.
10 papers.
- 2021LocalNewton: Reducing Communication Bottleneck for Distributed Learningcited by 15
- 2021ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systemscited by 5
- 2024Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Frameworkcited by 5
- 2025General Coded Computing: Adversarial Settingscited by 4
- 2023Gradient Coding with Iterative Block Leverage Score Samplingcited by 2
- 2023Randomized Polar Codes for Anytime Distributed Machine Learningcited by 1
- 2022A Learning-Based Approach to Approximate Coded Computationcited by 0
- 2025Optimal Approximate Matrix Multiplication over Sliding Windowscited by 0
- 2026High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Datacited by 0
- 2026Learning-Theoretic Foundation for General Coded Computing: The Straggler Settingcited by 0
- 2026Learning-Theoretic Foundation for General Coded Computing: The Straggler Setting28 Aug 2026
- 2026High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data14 Jan 2026
- 2025Optimal Approximate Matrix Multiplication over Sliding Windows26 Feb 2025
- 2025General Coded Computing: Adversarial Settings12 Feb 2025
- 2024Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework1 Jun 2024
- 2023Randomized Polar Codes for Anytime Distributed Machine Learning1 Sep 2023
- 2023Gradient Coding with Iterative Block Leverage Score Sampling6 Aug 2023
- 2022A Learning-Based Approach to Approximate Coded Computation19 May 2022
- 2021ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems20 Sep 2021
- 2021LocalNewton: Reducing Communication Bottleneck for Distributed Learning16 May 2021