Award Abstract # 1118043
CSR: Small: Collaborative Research: EEDAG: Exploring Energy-Efficient Parallel Tasks Generation and Scheduling for Heterogeneous Multicore Systems

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: TEXAS STATE UNIVERSITY
Initial Amendment Date: August 25, 2011
Latest Amendment Date: June 11, 2013
Award Number: 1118043
Award Instrument: Standard Grant
Program Manager: Anita La Salle
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2011
End Date: August 31, 2013 (Estimated)
Total Intended Award Amount: $56,708.00
Total Awarded Amount to Date: $73,208.00
Funds Obligated to Date: FY 2011 = $56,708.00
FY 2013 = $16,500.00
History of Investigator:
  • Ziliang Zong (Principal Investigator)
    zz11@txstate.edu
Recipient Sponsored Research Office: Texas State University - San Marcos
601 UNIVERSITY DR
SAN MARCOS
TX  US  78666-4684
(512)245-2314
Sponsor Congressional District: 15
Primary Place of Performance: Texas State University - San Marcos
601 UNIVERSITY DR
SAN MARCOS
TX  US  78666-4684
Primary Place of Performance
Congressional District:
15
Unique Entity Identifier (UEI): HS5HWWK1AAU5
Parent UEI:
NSF Program(s): Special Projects - CNS,
CSR-Computer Systems Research
Primary Program Source: 01001112DB NSF RESEARCH & RELATED ACTIVIT
01001314DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7354, 7923, 9178, 9251
Program Element Code(s): 171400, 735400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project addresses optimizing energy efficiency in the execution of parallel algorithms.

High energy cost is a salient constraint when running large scale parallel applications on the next generation of supercomputers that contain heterogeneous multicore processors and interconnections, motivating a rethinking of conventional approaches to modeling, designing and scheduling parallel tasks by taking energy-efficiency into consideration.

In this collaborative research, this team explores energy-efficient parallel task design, scheduling, and implementation and develops an power profiling tool (PowerPack) that can measure decomposed runtime power consumption of different computing components (e.g. processors, memory, networks and disks) when running large scale parallel applications.

The results of the research will be widely disseminated by maintaining an active project website, publishing peer-reviewed journal and conference papers, making the code available to other researchers, and presenting the research results in professional meetings. The availability of the research outcomes will provide ample opportunities for other researchers to further study the energy-efficiency of parallel applications. Through the collaboration of Texas State University ? San Marcos, Colorado School of Mines, and the Marquette University, PIs promote teaching, learning, and training by exposing graduate and undergraduate students to technological underpinnings in the fields of high performance computing in general and energy-efficient computing in particular. The close partnerships with a number of universities, data centers and national laboratories will also facilitate the broad dissemination of the proposed energy-efficient parallel tasks designing and scheduling techniques as well as the developed power profiling toolkits.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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I. Zecena, Z. L. Zong, R. Ge, T. D. Jin, Z. Z. Chen, and M. K. Qiu "Energy Consumption Analysis of Parallel Sorting Algorithms Running on Multicore Systems" Power Measurement and Profiling Workshop of the International Green Computing Conference (IGCC'12) , 2012
X. Li, J. Z. Ping, L. Ju, Y.H. Zhao and Z. L. Zong "Energy-Aware Scheduling for Precedence Constrained Parallel Tasks on Homogeneous Clusters" Chinese Journal of Computers , v.35 , 2012 , p.591

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

High energy cost has become a salient constraint when running large scale parallel applications on the next generation of supercomputers that contain heterogeneous multicore processors and interconnections. The primary goals of this collaborative CNS grant include: (1) investigating the impact of different parallel task design strategies on performance and energy-efficiency; (2) exploring energy-aware parallel scheduling algorithms for heterogeneous multicore systems; (3) developing an easy-to-use profiling toolkit that can obtain decomposed runtime power consumption characteristics of parallel tasks. Three universities (Texas State University, Marquette University and University of California - Riverside) are involved in this collaborative grant (#1118043, #1116691, and #1304969). The proposed research goals have been accomplished and the outcomes derived from this collaborative grant are summarized below:

 

1) Research Activities: Seven projects were supported, including energy consumption analysis of parallel sorting algorithms, energy-efficient scheduling for multicore systems with bounded resources, self-adaptive resource scheduling for heterogeneous cloud systems, eTune power analysis framework, characterizing energy consumption of MapReduce data movements, energy efficient parallel matrix multiplication for DVFS enabled clusters, and energy efficient parallel matrix multiplication via pipeline broadcasting. These projects have generated a number of novel algorithms and new studies, which contribute to the green computing discipline.  

 

2) Publications: By the time of submitting this report, seven peer-reviewed papers have been published in highly recognized IEEE/ACM sponsored conferences/workshops, which include the International Green Computing Conference (IGCC), the IEEE International Conference on Green Computing and Communications (GreenCom), the International Conference on Parallel Processing (ICPP), IEEE Cluster and the ACM Cloud and Autonomic Computing Conference (CAC). In addition, two conference papers have been submitted and currently under review.

 

3) Training: Twelve undergraduate students and eight graduate students participated in the aforementioned research projects led by the PIs (Zong, Ge and Chen). These research projects helped undergraduate students gain research experiences and interests in green computing. Many undergraduate students made impressive achievements and three of them are motivated to pursue graduate studies. Ryan Vogt, a junior at Marquette University in Fall 2013, was a co-author of a conference workshop paper and accepted by the Experiencing HPC for Undergraduates program of the Supercomputing conference (SC13). Patrick Millar, a senior at Marquette University, was a co-author of a poster submitted to SC13. Graduate students involved in the projects were allowed to improve in research background, skills, and publication records.

 

4) Education: The research findings derived from this collaborative grant have been integrated into various levels of classes taught by PIs at three institutions. Students from these classes are able to leverage the according topics to gain first-hand experience and intuition in the fundamental concepts and research frontiers of green computing.

 

5) Broad Impact: The PIs strive to attract minority students involved in the research projects supported by this grant. Ivan Zecena, a Hispanic student at Texas State University, is one of the exemplary minority students. Ivan first worked with PI Zong as an undergraduate research assistant in Fall 2011. He was motivated to conduct research, applied the master program and was accepted right after he received his Bachelor degree. During Ivan’s graduate study at Tex...

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