NDLI: GPUWattch:enabling energy optimizations in GPGPUs

Content Provider	ACM Digital Library
Author	Aamodt, Tor M. Reddi, Vijay Janapa Kim, Nam Sung Hetherington, Tayler Gilani, Syed Leng, Jingwen ElTantawy, Ahmed
Abstract	General-purpose GPUs (GPGPUs) are becoming prevalent in mainstream computing, and performance per watt has emerged as a more crucial evaluation metric than peak performance. As such, GPU architects require robust tools that will enable them to quickly explore new ways to optimize GPGPUs for energy efficiency. We propose a new GPGPU power model that is configurable, capable of cycle-level calculations, and carefully validated against real hardware measurements. To achieve configurability, we use a bottom-up methodology and abstract parameters from the microarchitectural components as the model's inputs. We developed a rigorous suite of 80 microbenchmarks that we use to bound any modeling uncertainties and inaccuracies. The power model is comprehensively validated against measurements of two commercially available GPUs, and the measured error is within 9.9% and 13.4% for the two target GPUs (GTX 480 and Quadro FX5600). The model also accurately tracks the power consumption trend over time. We integrated the power model with the cycle-level simulator GPGPU-Sim and demonstrate the energy savings by utilizing dynamic voltage and frequency scaling (DVFS) and clock gating. Traditional DVFS reduces GPU energy consumption by 14.4% by leveraging within-kernel runtime variations. More finer-grained SM cluster-level DVFS improves the energy savings from 6.6% to 13.6% for those benchmarks that show clustered execution behavior. We also show that clock gating inactive lanes during divergence reduces dynamic power by 11.2%.
Starting Page	487
Ending Page	498
Page Count	12
File Format	PDF
ISSN	01635964
DOI	10.1145/2508148.2485964
Journal	ACM SIGARCH Computer Architecture News (CARN)
Volume Number	41
Issue Number	3
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1981-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Energy Power estimation Gpu architecture Power Cuda
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

GPUWattch: enabling energy optimizations in GPGPUs

An integrated GPU power and performance model

An integrated GPU power and performance model

Buddy SM: Sharing Pipeline Front-End for Improved Energy Efficiency in GPGPUs

An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

Energy-driven integrated hardware-software optimizations using SimplePower

An Infrastructure for Tackling Input-Sensitivity of GPU Program Optimizations

CUDA Leaks: A Detailed Hack for CUDA and a (Partial) Fix

GPUWattch:enabling energy optimizations in GPGPUs

Similar Documents

GPUWattch: enabling energy optimizations in GPGPUs

An integrated GPU power and performance model

An integrated GPU power and performance model

Buddy SM: Sharing Pipeline Front-End for Improved Energy Efficiency in GPGPUs

An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

Energy-driven integrated hardware-software optimizations using SimplePower

An Infrastructure for Tackling Input-Sensitivity of GPU Program Optimizations

CUDA Leaks: A Detailed Hack for CUDA and a (Partial) Fix

GPUWattch:enabling energy optimizations in GPGPUs