Abstract: As high performance heterogeneous computing systems continually become faster, the operating cost to run these systems has increased. A significant portion of
the operating costs can be attributed to the amount of energy required for these systems to operate. To reduce these costs it is important for system
administrators to operate these systems in an energy efficient manner. Additionally, it is important to be able to measure the performance of a given system so
that the impacts of operating at different levels of energy efficiency can be analyzed. The goal of this research is to examine how energy and system
performance interact with each other for a variety of environments. One part of the study considers a computing system and its corresponding workload based on
the expectations for future environments of Department of Energy and Department of Defense interest. New heuristics are presented that maximize a
performance metric created using utility functions. A framework has been established to analyze the trade-offs between performance (utility earned) and energy
consumption. Furthermore, previous heuristics have been adapted, and new heuristics and energy filtering techniques have been designed for a computing
system that has the goal of maximizing the total utility earned while being subject to an energy constraint. Stochastic models for execution time and power
consumption are used to create "fuzzy" Pareto fronts to analyze the variability of solutions along the Pareto front when uncertainties in execution time and power
consumption are present within a system. In addition to using utility earned as a measure of system performance, system makespan has also been studied. The
trade-offs between minimizing makespan and minimizing energy for various environments has been analyzed. Finally, a framework has been developed that
enables the investigation of the effects of P-states and memory interference on energy consumption and system performance.
Adviser: H.J. Siegel Co-Adviser: Tony Maciejewski Non-ECE Member: Sudeep Pasricha, ECE Member 3: Patrick Burns, VP for Information Technologies/ Dean of Libraries Addional Members: Greg Koenig
Publications: Journal Papers:
[3] B. Khemka, R. Friese, S. Pasricha, A. A. Maciejewski, H. J. Siegel, G. A. Koenig, S. Powers, M. M. Hilton, R. Rambharos, and S. Poole, “Utility maximizing dynamic resource management in an oversubscribed energy-constrained heterogeneous computing system,†Sustainable Computing: Informatics and Systems, Elsevier, 2014, accepted for publication.
[2] B. Khemka, R. Friese, L. D. Briceño, H. J. Siegel, A. A. Maciejewski, G. A. Koenig, C. Groer, G. Okonski, M. M. Hilton, R. Rambharos, and S. Poole, “Utility functions and resource management in an oversubscribed heterogeneous computing environment,†IEEE Transactions on Computers, 2014, accepted for publication.
[1] P. Maxwell, A. A. Maciejewski, H. J. Siegel, J. Potter, G. Pfister, J. Smith, and R. Friese, “Robust static planning tool for military village search missions: Model and heuristics,†Journal of Defense Modeling and Simulation, SAGE, vol. 10, no. 1, pp. 31-47, Jan. 2013.
Book Chapters:
[1] K. M. Tarplee, R. Friese, A. A. Maciejewski, and H. J. Siegel, “Efficient and scalable Pareto front generation for energy and makespan in heterogeneous computing systems,†in Recent Advances in Computational Optimization (S. Fidanova, ed.), Studies in Computational Intelligence Series, Springer, 2014, accepted for publication.
Conference Publications:
[10] D. Dauwe, E. Jonardi, R. Friese, S. Pasricha, A. A. Maciejewski, D. Bader, and H. J. Siegel, “A methodology for co-location aware application performance modeling in multicore computing,†in 17th IEEE Workshop on Advances in Parallel and Distributed Computational Models (APDCM 2015), Hyderabad, India, May 2015, 10 pages.
[9] H. J. Siegel, B. Khemka, R. Friese, S. Pasricha, A. A. Maciejewski, G. A. Koenig, S. Powers, M. Hilton, J. Rambharos, G. Okonski, and S. W. Poole, “Energy-aware resource management for computing systems,†in 7th International Conference on Contemporary Computing (IC3), Noida, India, Aug. 2014, pp. 7-12, (Siegel Keynote).
[8] D. Dauwe, R. Friese, S. Pasricha, A. A. Maciejewski, G. A. Koenig, and H. J. Siegel, “Modeling the effects on power and performance from memory interference of co-located applications in multicore systems,†in The 2014 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2014), Las Vegas, NV, July 2014, pp. 3-9.
[7] B. Khemka, R. Friese, S. Pasricha, A. A. Maciejewski, H. J. Siegel, G. A. Koenig, S. Powers, M. Hilton, R. Rambharos, and S. Poole, “Utility driven dynamic resource management in an oversubscribed energy-constrained heterogeneous system,†in 23rd IEEE Heterogeneity in Computing Workshop (HCW 2014), Phoenix, AZ, May 2014, pp. 58-67.
[6] K. M. Tarplee, R. Friese, A. A. Maciejewski, and H. J. Siegel, “Efficient and scalable computation of the energy and makespan Pareto front for heterogeneous computing systems,†in 6 Workshop on Computational Optimization (WCO ‘13), Krakow, Poland, Sep. 2013, pp. 401-408, received “The 2013 Zdzislaw Pawlak Best Paper Award,†by the Award Committee the of 8th Symposium on Advances in Artificial Intelligence and Applications.
[5] R. Friese, T. Brinks, C. Oliver, A. A. Maciejewski, H. J. Siegel, and S. Pasricha, “A machine-by-machine analysis of a bi-objective resource allocation problem,†in The 2013 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2013), Las Vegas, NV, July 2013, pp. 3-9.
[4] R. Friese, B. Khemka, A. A. Maciejewski, H. J. Siegel, G. A. Koenig, S. Powers, M. Hilton, J. Rambharos, G. Okonski, and S. W. Poole, “An analysis framework for investigating the trade-offs between system performance and energy consumption in a heterogeneous computing environment,†in 22nd IEEE Heterogeneity in Computing Workshop (HCW 2013), Boston, MA, May 2013, pp. 19-30.
[3] R. Friese, T. Brinks, C. Oliver, H. J. Siegel, and A. A. Maciejewski, “Analyzing the trade-offs between minimizing makespan and minimizing energy consumption in a heterogeneous resource allocation problem,†in The 2nd International Conference on Advanced Communications and Computation (INFOCOMP 2012), Venice, Italy, Oct. 2012, pp. 81-89, received one of seven best paper awards given.
[2] R. Friese, P. Maxwell, A. A. Maciejewski, and H. J. Siegel, “A graphical user interface for simulating robust military village searches,†in International Conference on Modeling, Simulation and Visualization Methods (MSV ‘11), Las Vegas, NV, July 2011, pp. 75-81.
[1] P. Maxwell, R. Friese, A. A. Maciejewski, H. J. Siegel, J. Potter, and J. Smith, “A demonstration of a simulation tool for planning robust military village searches,†in Huntsville Simulation Conference (HSC ’10), Huntsville, AL, Oct. 2010, 9 pages.
Program of Study: CS 540 CS 645 ECE 555 ECE 699 ECE 799 MATH 560 GRAD 544C GSTR 600
