D. Li, D. Nikolopoulos, K.W. Cameron, B. R. de Supinski and M. Schulz. Efficient Memory Registration for High Performance Networks Using Helper Threads,. ACM International Conference on Computing Frontiers (CF’11), 12 pages, May 2011.
Archive for the 'Publications' Category
A. Vishnu, *S. Song, A. Marquez, K. Barker, D. Kerbyson, K.W. Cameron, P. Balaji, Designing Energy Efficient Communication Runtime Systems for Data Centric Programming Models, IEEE/ACM International Conference on Green Computing and Communications (GreenCom 2010), Hangzhou, China, 12 pages, December 2010.
*S. Song, *C.-Y. Su, *R. Ge, A. Vishnu, and K.W. Cameron, Iso-energy-efficiency: An approach to power-constrained parallel computation, Proceedings of 25th IEEE International Parallel and Distributed Processing Symposium (IPDPS 11), 12 pages, May 2011.
D. Li, R. Ge, and K.W. Cameron, System-level, Unified In-band and Out-of-band Dynamic Thermal Control, proceedings of 2007 International Conference on Parallel Processing (ICPP 07), September 2010.
Z. Cao, D. R. Easterling, L. T. Watson, D. Li, K. W. Cameron, and W.-C. Feng, Power saving experiments for large scale global optimization”, International Journal of Parallel Emergent Distributed Systems, to appear (2010).
Abstract
Green computing, an emerging field of research that seeks to reduce excess power consumption in high-performance computing, is gaining popularity among researchers. Research in this field often relies on simulation or only uses a small cluster, typically 8 or 16 nodes, because of the lack of hardware support. In contrast, System G at Virginia Tech is a 2592 processor supercomputer equipped with power-aware components suitable for large-scale green computing research. DIRECT is a deterministic global optimisation algorithm, implemented in the mathematical software package VTDIRECT95. This paper explores the potential energy savings for the parallel implementation of DIRECT, called pVTdirect, when used with a large-scale computational biology application, parameter estimation for a budding yeast cell cycle model, on System G. Two power-aware approaches for pVTdirect are developed and compared against the CPUSPEED power saving system tool. The results show that knowledge of the parallel workload of the underlying application is beneficial for power management.
Ge, R., Feng, X., Song, S., Chang, H-C., Li, D., Cameron, K.W., PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications. IEEE Transactions on Parallel and Distributed Systems, IEEE Computer Society, 21(5): 658-671 (2010).
Abstract
Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance.
D. Li, B. R. de Supinski, M. Schulz, K. W. Cameron, D. S. Nikolopoulos, Hybrid MPI/OpenMP Power-Aware Computing. Proceedings of 24th IEEE International Parallel and Distributed Processing Symposium (IPDPS 10), April 2010.
D. Li, D. Nikolopoulos, K. W. Cameron, B. R. de Supinski, M. Schulz, Power-aware MPI Task Aggregation Prediction for High-End Computing Systems. Proceedings of 24th IEEE International Parallel and Distributed Processing Symposium (IPDPS 10), April 2010.
Song, S., Ge, R., Feng, X., Cameron, K.W., Energy profiling and analysis of the HPC Challenge Benchmarks, International Journal of High Performance Computing Applications, Sage Publications, New York, 2009, 23(3): 265-276 (2009).
Z. Cao, L. T. Watson, K. W. Cameron, R. Ge: A power aware study for VTDIRECT95 using DVFS. Proceedings of SpringSim 2009