Machine Learning GPU Power Measurement on Chameleon Cloud

J. Y. Chuah
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引用次数: 2

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.
基于变色龙云的机器学习GPU功耗测量
机器学习(ML)对许多工业和科学努力变得至关重要,并且在高性能计算(HPC)环境中占有越来越重要的地位。对于大型数据集,神经网络训练需要很长的执行时间,像TensorFlow这样的库实现GPU加速以减少每次计算的总运行时间。本教程演示了如何1)使用变色龙云执行跨不同硬件配置的机器学习训练性能的比较研究;2)在NVIDIA gpu上运行并监控TensorFlow的功耗使用情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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