NDLI: Energy Efficiency Analysis of GPUs

Content Provider	IEEE Xplore Digital Library
Author	Cebrian, J.M. Guerrero, G.D. Garcia, J.M.
Copyright Year	2012
Abstract	In the last few years, Graphics Processing Units (GPUs) have become a great tool for massively parallel computing. GPUs are specifically designed for throughput and face several design challenges, specially what is known as the Power and Memory Walls. In these devices, available resources should be used to enhance performance and throughput, as the performance per watt is really high. For massively parallel applications or kernels, using the available silicon resources for power management was unproductive, as the main objective of the unit was to execute the kernel as fast as possible. However, not all the applications that are being currently ported to GPUs can make use of all the available resources, either due to data dependencies, bandwidth requirements, legacy software on new hardware, etc, reducing the performance per watt. This new scenario requires new designs and optimizations to make these GPGPU's\footnote{General Purpose Graphics Processing Units} more energy efficient. But first comes first, we should begin by analyzing the applications we are running on these processors looking for bottlenecks and opportunities to optimize for energy efficiency. In this paper we analyze some kernels taken from the CUDA SDK\footnote{Software Development Kit} in order to discover resource underutilization. Results show that this underutilization is present, and resource optimization can increase the energy efficiency of GPU-based computation. We then discuss different strategies and proposals to increase energy efficiency in future GPU designs.
Starting Page	1014
Ending Page	1022
File Size	894435
Page Count	9
File Format	PDF
ISBN	9781467309745
DOI	10.1109/IPDPSW.2012.124
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power Dissipation Bandwidth Benchmark testing Hardware Power dissipation Kernel Energy Efficiency GPU GPGPU Graphics processing unit Clocks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Micro-benchmark Suite for AMD GPUs

Memory Access Characterization of Scientific Applications on GPU and Its Implication on Low Power Optimization

Energy Analysis of Parallel Scientific Kernels on Multiple GPUs

Kernel Fusion: An Effective Method for Better Power Efficiency on Multithreaded GPU

Computing energy-efficiency in the mobile GPU

Quantifying the impact of GPUs on performance and energy efficiency in HPC clusters

Power analysis and optimizations for GPU architecture using a power simulator

A Taxonomy of GPGPU Performance Scaling

Fixing Performance Bugs: An Empirical Study of Open-Source GPGPU Programs

Energy Efficiency Analysis of GPUs

Similar Documents

A Micro-benchmark Suite for AMD GPUs

Memory Access Characterization of Scientific Applications on GPU and Its Implication on Low Power Optimization

Energy Analysis of Parallel Scientific Kernels on Multiple GPUs

Kernel Fusion: An Effective Method for Better Power Efficiency on Multithreaded GPU

Computing energy-efficiency in the mobile GPU

Quantifying the impact of GPUs on performance and energy efficiency in HPC clusters

Power analysis and optimizations for GPU architecture using a power simulator

A Taxonomy of GPGPU Performance Scaling

Fixing Performance Bugs: An Empirical Study of Open-Source GPGPU Programs

Energy Efficiency Analysis of GPUs