MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Tschand, Arya Rajan, Arun Tejusve Raghunath Idgunji, Sachin Ghosh, Anirban Holleman, Jeremy Kiraly, Csaba Ambalkar, Pawan Borkar, Ritika Chukka, Ramesh Cockrell, Trevor Curtis, Oliver Fursin, Grigori Hodak, Miro Kassa, Hiwot Lokhmotov, Anton Miskovic, Dejan Pan, Yuechao Manmathan, Manu Prasad Raymond, Liz John, Tom St. Suresh, Arjun Taubitz, Rowan Zhan, Sean Wasson, Scott Kanter, David Reddi, Vijay Janapa |
| author_facet | Tschand, Arya Rajan, Arun Tejusve Raghunath Idgunji, Sachin Ghosh, Anirban Holleman, Jeremy Kiraly, Csaba Ambalkar, Pawan Borkar, Ritika Chukka, Ramesh Cockrell, Trevor Curtis, Oliver Fursin, Grigori Hodak, Miro Kassa, Hiwot Lokhmotov, Anton Miskovic, Dejan Pan, Yuechao Manmathan, Manu Prasad Raymond, Liz John, Tom St. Suresh, Arjun Taubitz, Rowan Zhan, Sean Wasson, Scott Kanter, David Reddi, Vijay Janapa |
| contents | Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI Tschand, Arya Rajan, Arun Tejusve Raghunath Idgunji, Sachin Ghosh, Anirban Holleman, Jeremy Kiraly, Csaba Ambalkar, Pawan Borkar, Ritika Chukka, Ramesh Cockrell, Trevor Curtis, Oliver Fursin, Grigori Hodak, Miro Kassa, Hiwot Lokhmotov, Anton Miskovic, Dejan Pan, Yuechao Manmathan, Manu Prasad Raymond, Liz John, Tom St. Suresh, Arjun Taubitz, Rowan Zhan, Sean Wasson, Scott Kanter, David Reddi, Vijay Janapa Hardware Architecture Distributed, Parallel, and Cluster Computing Machine Learning Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems. |
| title | MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI |
| topic | Hardware Architecture Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2410.12032 |