Abstract
Power consumption is a first class design constraint for developing future exascale computing systems. To achieve exascale system performance with realistic power provisioning of 20–30 MW, we need to improve power-performance efficiency significantly compared to today’s supercomputer systems. In order to maximize effective performance within a power constraint, investigating how to optimize power resource allocation to each hardware component or each job submitted to the system is necessary. We have been conducting research and development on a software framework for code optimization and system power management for the power-constraint adaptive systems. We briefly introduce the research efforts for maximizing application performance under a given power constraint, power-aware resource manager, and power-performance simulation and analysis framework for future supercomputer systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, T., He, Y., Kondo, M.: Demand-aware power management for power-constrained HPC systems. In: Proceedings of the 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid2016), Cartagena, pp. 21–31 (2016)
Cao, T., Huang, W., He, Y., Kondo, M.: Cooling-aware job scheduling and node allocation for overprovisioned HPC systems. In: Prodeedings of 31st IEEE International Parallel & Distributed Processing Symposium (IPDPS 2017), Orlando (2017)
Casanova, H., Giersch, A., Legrand, A., Quinson, M., Suter, F.: Versatile, scalable, and accurate simulation of distributed applications and platforms. J. Parallel Distrib. Comput. 74(10), 2899–2917 (2014)
Inadomi, Y., Patki, T., Inoue, K., Aoyagi, M., Rountree, B., Schulz, M., Lowenthal, D., Wada, Y., Fukazawa, K., Ueda, M., Kondo, M., Miyoshi, I.: Analyzing and mitigating the impact of manufacturing variability in power-constrained supercomputing. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC15), Austin (2015)
Intel 64 and IA-32 architectures software developers manual. Intel, vol. 3, Mar 2013
Kogge, P.M.: Architectural challenges at the exascale frontier. In: Simulating the Future: Using One Million Cores and Beyond (invited talk) (2008)
Miwa, S., Aita, S., Nakamura, H.: Performance estimation for high performance computing systems with energy efficient ethernet technology. J. Comput. Sci. Res. Dev. 29(3–4), 161–169 (2014)
Miwa, S., Nakamura, H.: Profile-based power shifting in interconnection networks with on/off links. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC15), Austin (2015)
Sakamoto, R., Cao, T., Kondo, M., Inoue, K., Ueda, M., Patki, T., Ellsworth, D., Rountree, B., Schulz, M.: Production hardware overprovisioning: real-world performance optimization using an extensible power-aware resource management framework. In: Proceedings of the 31st IEEE International Parallel & Distributed Processing Symposium (IPDPS 2017), Orlando (2017)
Sakamoto, R., Patki, T., Cao, T., Kondo, M., Inoue, K., Ueda, M., Ellsworth, D., Rountree, B., Schulz, M.: Analyzing resource trade-offs in hardware overprovisioned supercomputers. In: Proceedings of the 32nd IEEE International Parallel & Distributed Processing Symposium (IPDPS2018), Orlando (2017)
Saravanan, K.P., Carpenter, P.M., Ramirez, A.: Power/performance evaluation of energy efficient ethernet (EEE) for high performance computing. In: Proceedings of the 2013 IEEE International Symposium on Performance Analysis of Systems and Software, Austin, pp. 205–214 (2013)
Saravanan, K.P., Carpenter, P.M., Ramirez, A.: A performance perspective on energy efficient HPC links. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC14), New Orleans, pp. 313–322 (2014)
Shende, S.S., Malony, A.D.: The TAU parallel performance system. Int. J. High Perform. Comput. Appl. 20(2) (2006). https://www.cs.uoregon.edu/research/tau/home.php
Totoni, E., Jain, N., Kale, L.: Power management of extreme-scale networks with on/off links in runtime systems. ACM Trans. Parallel Comput. 1(2), 16 (2015)
Wada, Y., He, Y., Cao, T., Kondo, M.: A power management framework with simple DSL for automatic power-performance optimization on power-constrained HPC systems. In: Proceedings of Supercomputing Asia (SCA18) (2018)
Yoo, A., Jette, M., Grondona, M.: SLURM: simple linux utility for resource management. In: Job Scheduling Strategies for Parallel Processing, Seattle. Lecture Notes in Computer Science, vol. 2862, pp. 44–60 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Kondo, M., Miyoshi, I., Inoue, K., Miwa, S. (2019). Power Management Framework for Post-petascale Supercomputers. In: Sato, M. (eds) Advanced Software Technologies for Post-Peta Scale Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-1924-2_13
Download citation
DOI: https://doi.org/10.1007/978-981-13-1924-2_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1923-5
Online ISBN: 978-981-13-1924-2
eBook Packages: Computer ScienceComputer Science (R0)