Cited By
View all- Khan RYazdani AFu YPaul AJi BJian XCheng YButt ANaor DGoel A(2023)SHADEProceedings of the 21st USENIX Conference on File and Storage Technologies10.5555/3585938.3585947(135-151)Online publication date: 21-Feb-2023
Machine Learning GPU Power Measurement on Chameleon Cloud
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Abstract
Machine Learning (ML) is becoming critical for many industrial and scientific endeavors, and has a growing presence in High Performance Computing (HPC) environments. Neural network training requires long execution times for large data sets, and libraries like TensorFlow implement GPU acceleration to reduce the total runtime for each calculation. This tutorial demonstrates how to 1) use Chameleon Cloud to perform comparative studies of ML training performance across different hardware configurations; and 2) run and monitor power utilization of TensorFlow on NVIDIA GPUs.
Index Terms
- Machine Learning GPU Power Measurement on Chameleon Cloud
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Information
Published In
December 2017
222 pages
ISBN:9781450351492
DOI:10.1145/3147213
- General Chairs:
- Ashiq Anjum,
- Alan Sill,
- Program Chairs:
- Geoffrey Fox,
- Yong Chen
Copyright © 2017 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
- SIGARCH: ACM Special Interest Group on Computer Architecture
- IEEE TCSC: IEEE Technical Committee on Scalable Computing
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 December 2017
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- Tutorial
Funding Sources
Conference
UCC '17
Sponsor:
- SIGARCH
- IEEE TCSC
UCC '17: 10th International Conference on Utility and Cloud Computing
December 5 - 8, 2017
Texas, Austin, USA
Acceptance Rates
UCC '17 Paper Acceptance Rate 17 of 63 submissions, 27%;
Overall Acceptance Rate 38 of 125 submissions, 30%
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Cited By
View all- Khan RYazdani AFu YPaul AJi BJian XCheng YButt ANaor DGoel A(2023)SHADEProceedings of the 21st USENIX Conference on File and Storage Technologies10.5555/3585938.3585947(135-151)Online publication date: 21-Feb-2023
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