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.

Project Report

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 Texas State University, he has published two papers and won several awards, which include the travel award of IEEE International Symposium on Workload Characterization (2012) and the Texas State University Best Research Poster Award for Minority Students (2013). He was also accepted by the competitive Broader Engagement program of the Supercomputing conference (SC12) and the NSF supported Extreme Science and Engineering Discovery Environment (XSEDE) program. Ivan graduated in August 2013 with his master degree from Texas State University. Heather Bort, a female graduate, was accepted by both the Grace Hopper Celebration of Women in Computing and Broader Engagement program of the Supercomputing conference (SC13). 6) Released Data: The research findings of this project are published in conferences or workshops for public knowledge. The energy efficient scheduling code has been released to the community at In summary, the PIs (Zong, Ge and Chen) from Texas State University, Marquette University and University of California - Riverside collaborate closely on this grant and have successfully accomplished the proposed research goals. The research projects supported by this grant provide numerous opportunities to undergraduate and graduate students at three institutions to participate and gain valuable experiences in high performance computing research in general and green computing research in particular. The research approaches and results have been introduced into both undergraduate and graduate level courses to benefit a large group of students at three institutions. All published papers have been made publicly accessible through the Internet, contributing to national and international green computing research community.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
Standard Grant (Standard)
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Anita J. LaSalle
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Texas State University - San Marcos
San Marcos
United States
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