Machine Learning GPU Power Measurement on Chameleon Cloud Extended Abstract

被引：1
|
作者：
Chuah, Joon Yee [1 ]
机构：
[1] Texas Adv Comp Ctr, Austin, TX 78758 USA
来源：
PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC' 17) | 2017年
基金：
美国国家科学基金会;
关键词：
ACM proceedings; machine learning; GPU; power;
D O I：
10.1145/3147213.3149450
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：181 / 181
页数：1
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