Verified instruction-level energy consumption measurement for NVIDIA GPUs-Reference-Cited by-同舟云学术

Verified instruction-level energy consumption measurement for NVIDIA GPUs

Published:2020-05-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Computing Frontiers
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Author:

Arafa Yehia¹,ElWazir Ammar¹,ElKanishy Abdelrahman¹,Aly Youssef²,Elsayed Ayatelrahman¹,Badawy Abdel-Hameed¹,Chennupati Gopinath³,Eidenbenz Stephan³,Santhi Nandakishore³

Affiliation:

1. New Mexico State University

2. Arab Academy for Science, Technology & Maritime Transport, Egypt

3. Los Alamos National Laboratory

Funder

Triad National Security, LLC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3387902.3392613

Reference45 articles.

1. A. Agelastos B. Allan J. Brandt P. Cassella J. Enos J. Fullop A. Gentile S. Monk N. Naksinehaboon J. Ogden M. Rajan M. Showerman J. Stevenson N. Taerat and T. Tucker. 2014. The Lightweight Distributed Metric Service: A Scalable Infrastructure for Continuous Monitoring of Large Scale Computing Systems and Applications. In SC '14: Proceedings of the International Conference for High Performance Computing Networking Storage and Analysis. 154--165. 10.1109/SC.2014.18 A. Agelastos B. Allan J. Brandt P. Cassella J. Enos J. Fullop A. Gentile S. Monk N. Naksinehaboon J. Ogden M. Rajan M. Showerman J. Stevenson N. Taerat and T. Tucker. 2014. The Lightweight Distributed Metric Service: A Scalable Infrastructure for Continuous Monitoring of Large Scale Computing Systems and Applications. In SC '14: Proceedings of the International Conference for High Performance Computing Networking Storage and Analysis. 154--165. 10.1109/SC.2014.18

2. Fast, accurate, and scalable memory modeling of GPGPUs using reuse profiles

3. PPT-GPU

4. Low Overhead Instruction Latency Characterization for NVIDIA GPGPUs

5. PPT-GPU: Scalable GPU Performance Modeling

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