SC23 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Technical Papers Archive

Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service


Authors: Baolin Li (Northeastern University); Siddharth Samsi and Vijay Gadepally (Massachusetts Institute of Technology (MIT), Lincoln Laboratory); and Devesh Tiwari (Northeastern University)

Abstract: This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to carbon footprint. We introduce, Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets.




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