Power Modeling for GPU Architectures Using McPAT

Author:

Lim Jieun1,Lakshminarayana Nagesh B.2,Kim Hyesoon2,Song William2,Yalamanchili Sudhakar2,Sung Wonyong1

Affiliation:

1. Seoul National University, Seoul, South Korea

2. Georgia Institute of Technology, Atlanta, GA

Abstract

Graphics Processing Units (GPUs) are very popular for both graphics and general-purpose applications. Since GPUs operate many processing units and manage multiple levels of memory hierarchy, they consume a significant amount of power. Although several power models for CPUs are available, the power consumption of GPUs has not been studied much yet. In this article we develop a new power model for GPUs by utilizing McPAT, a CPU power tool. We generate initial power model data from McPAT with a detailed GPU configuration, and then adjust the models by comparing them with empirical data. We use the NVIDIA's Fermi architecture for building the power model, and our model estimates the GPU power consumption with an average error of 7.7% and 12.8% for the microbenchmarks and Merge benchmarks, respectively.

Funder

Brain Korea 21 Project

Division of Computer and Network Systems

Division of Computing and Communication Foundations

Sandia National Laboratories, National Nuclear Security Administration

Semiconductor Research Corporation

National Research Foundation of Korea

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Cited by 50 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Power overwhelming: the one with the oscilloscopes;Journal of Visualization;2024-08-10

2. TAO: Re-Thinking DL-based Microarchitecture Simulation;ACM SIGMETRICS Performance Evaluation Review;2024-06-11

3. TAO: Re-Thinking DL-based Microarchitecture Simulation;Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems;2024-06-10

4. TAO: Re-Thinking DL-based Microarchitecture Simulation;Proceedings of the ACM on Measurement and Analysis of Computing Systems;2024-05-21

5. An automated and portable method for selecting an optimal GPU frequency;Future Generation Computer Systems;2023-12

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3