PowerCoord: Power capping coordination for multi-CPU/GPU servers using reinforcement learning-Reference-Cited by-同舟云学术

PowerCoord: Power capping coordination for multi-CPU/GPU servers using reinforcement learning

Published:2020-12 Issue: Volume:28 Page:100412
ISSN:2210-5379
Container-title:Sustainable Computing: Informatics and Systems
language:en
Short-container-title:Sustainable Computing: Informatics and Systems

Author:

Azimi Reza,Jing Chao,Reda Sherief

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,General Computer Science

Reference37 articles.

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3. RAPL: memory power estimation and capping;David;2010 ACM/IEEE International Symposium on Low-Power Electronics and Design (ISLPED),2010

4. Maximizing performance under a power cap: a comparison of hardware, software, and hybrid techniques;Zhang;ACM SIGARCH Computer Architecture News, vol. 44(2),2016

5. A coordinated power capping controller for multi-CPU/GPU servers;Azimi;2018 Ninth International Green and Sustainable Computing Conference (IGSC),2018

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1. A multithreaded CUDA and OpenMP based power‐aware programming framework for multi‐node GPU systems;Concurrency and Computation: Practice and Experience;2023-08-29

2. Real-time power optimization for application server clusters based on Mixed-Integer Programming;Future Generation Computer Systems;2022-12

3. Combining Uncore Frequency and Dynamic Power Capping to Improve Power Savings;2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2022-05

4. Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach;IEEE Transactions on Parallel and Distributed Systems;2022-04-01

5. The Case for Cross-Component Power Coordination on Power Bounded Systems;IEEE Transactions on Parallel and Distributed Systems;2021-10-01