Award Abstract # 1551182
SHF:Small:Collaborative Research: Application-aware Energy Modeling and Power Management for Parallel and High Performance Computing

NSF Org: CCF
Division of Computing and Communication Foundations
Recipient: OAKLAND UNIVERSITY
Initial Amendment Date: July 21, 2015
Latest Amendment Date: July 21, 2015
Award Number: 1551182
Award Instrument: Standard Grant
Program Manager: Almadena Chtchelkanova
achtchel@nsf.gov
 (703)292-7498
CCF
 Division of Computing and Communication Foundations
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: June 1, 2015
End Date: May 31, 2018 (Estimated)
Total Intended Award Amount: $249,875.00
Total Awarded Amount to Date: $249,875.00
Funds Obligated to Date: FY 2014 = $142,085.00
History of Investigator:
  • Yonghong Yan (Principal Investigator)
    yyan7@uncc.edu
Recipient Sponsored Research Office: Oakland University
2200 N SQUIRREL RD
ROCHESTER
MI  US  48309-4401
(248)370-4116
Sponsor Congressional District: 11
Primary Place of Performance: Oakland University
2200 N. Squirrel Rd.
Rochester
MI  US  48309-4401
Primary Place of Performance
Congressional District:
11
Unique Entity Identifier (UEI): HJTLACN81NK1
Parent UEI: LY1HEB9XS5G8
NSF Program(s): Software & Hardware Foundation
Primary Program Source: 01001415DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7923, 7942
Program Element Code(s): 779800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

One of the critical challenges in scaling out current and future high performance computing (HPC) and enterprise computing systems is the requirement that their power envelope remain comparable to that of today?s systems. This project addresses this ?power wall? challenge from the system software aspect by developing application-aware methodologies of energy modeling and power management. The project optimizes system efficiency by tuning performance and energy consumption to resonate with application runtime behavior while staying below the system power envelope. The project develops user interfaces and new compiler models and runtime tuning techniques to manage the tradeoffs between performance and energy consumption. The approach enables cooperative, application-specific control of energy consumption between hardware, system software and applications. The investigations and solutions deepen understanding of application power usage and guide users to customized performance and energy consumption behavior.

This collaborative project integrates the development, education, and outreach efforts of collaborating University partners and is well positioned to have a substantial impact on both the HPC research community and hardware designers and vendors. All findings are published in peer-reviewed conferences and journals while source code and results are available through a project web site. This work addresses the need for energy efficiency improvements in large-scale systems in support of high-end simulations used to design pharmaceuticals, aircraft, global warming scenarios, etc. The proposed techniques influence the design of future directions HPC and enterprise computing systems from industry and government. The project engages and trains graduate and undergraduate students, including underrepresented minority students, in the area of energy efficient computing, parallel and high performance computing, and computer architecture and systems. The open source evaluation platforms are used in teaching related coursework in graduate and undergraduate classes.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Yan, Yonghong and Liu, Jiawen and Cameron, Kirk W. and Umar, Mariam "HOMP: Automated Distribution of Parallel Loops and Data in Highly Parallel Accelerator-Based Systems" Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International , 2017 10.1109/IPDPS.2017.99 Citation Details
Yi, Xinyao and Stokes, David and Yan, Yonghong and Liao, Chunhua "CUDAMicroBench: Microbenchmarks to Assist CUDA Performance Programming" 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) , 2021 https://doi.org/10.1109/IPDPSW52791.2021.00068 Citation Details

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page