Abstract
In an era where power and energy are the first-class constraints of computing systems, precise power information is crucial for energy efficiency optimization in parallel computing systems. Current power monitoring techniques rely on either software-centric power models that suffer from poor accuracy or hardware measurement schemes that have coarse granularity. This paper introduces PowerDis, a new technique for accurately measuring and forecasting the power consumption of computing components, such as CPUs and memory, within the compute node on parallel computing systems. PowerDis combines coarse-grained node power readings collected through hardware measurement and a software power disaggregation model to improve monitoring granularity while achieving high accuracy. Additionally, it can be used to predict future power usage by mining spatiotemporal patterns with minimal modifications to dataset construction building on the existing PowerDis framework. We evaluate PowerDis on both ARM-based and X86-based platforms. Extensive results show that PowerDis has great accuracy for power estimation and forecasting, reducing the mean absolute percentage error (MAPE) by up to 20% compared to other power modeling methods.
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
Wu, F., et al.: A holistic energy-efficient approach for a processor-memory system. Tsinghua Sci. Technol. 24(4), 468–483 (2019)
Gholkar, N., et al.: Uncore power scavenger: a runtime for uncore power conservation on HPC systems. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019, New York, NY, USA (2019)
Zhou, L., et al.: Gemini: learning to manage CPU power for latency-critical search engines. In: 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 637–349 (2020)
Juan, C., et al.: More bang for your buck: boosting performance with capped power consumption. Tsinghua Sci. Technol. 26(3), 370 (2021)
Ilsche, T., et al.: Power measurement techniques for energy-efficient computing: reconciling scalability, resolution, and accuracy. Comput. Sci. Res. Dev. 34, 03 (2019)
Rotem, E., et al.: Power-management architecture of the intel microarchitecture code-named sandy bridge. IEEE Micro 32(2), 20–27 (2012)
Sagi, M., et al.: A lightweight nonlinear methodology to accurately model multicore processor power. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39(11), 3152–3164 (2020)
Sagi, M., et al.: Long short-term memory neural network-based power forecasting of multi-core processors. In: 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1685–1690 (2021)
Haj-Yihia, J., Yasin, A., Asher, Y.B., Mendelson, A.: Fine-grain power breakdown of modern out-of-order cores and its implications on skylake-based systems. ACM Trans. Archit. Code Optim. 13(4) (2016)
Spec cpu 2017 (2017). http://www.spec.org/cpu2017/
The princeton application repository for shared-memory computers (2020). https://parsec.oden.utexas.edu/
Luszczek, P., et al.: S12-the HPC challenge (HPCC) benchmark suite. In: Proceedings of the ACM/IEEE SC2006 Conference on High Performance Networking and Computing, November 11–17, 2006, Tampa, FL, USA (2006)
Graph500 (2017). https://graph500.org/
Jack, D., et al.: HPL-AI mixed-precision benchmark (2019). https://icl.bitbucket.io/hpl-ai/
High performance conjugate gradients (2020). https://www.hpcg-benchmark.org/
Powell, M.D., et al.: Camp: a technique to estimate per-structure power at run-time using a few simple parameters. In: 15th International Conference on High-Performance Computer Architecture (HPCA-15 2009), 14–18 February 2009, Raleigh, North Carolina, USA (2009)
McCullough, J.C., et al.: Evaluating the effectiveness of model-based power characterization. In: Proceedings of the 2011 USENIX Conference on USENIX Annual Technical Conference, USENIX ATC’11, page 12, USA, 2011. USENIX Association (2011)
Wu, X., Taylor, V., Lan, Z.: Performance and power modeling and prediction using mummi and 10 machine learning methods. Concurr. Comput. Pract. Exp. 35, e7254 (2022)
Borghesi, A. Bartolini,, A., et al.: Predictive modeling for job power consumption in HPC systems, pp. 181–199 (2016)
Song, S., et al.: A simplified and accurate model of power-performance efficiency on emergent GPU architectures. In: 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, pp. 673–686 (2013)
Yang, X.J., Liao, X.-K., et al.: The tianhe-1a supercomputer: Its hardware and software. J. Comput. Sci. Technol. 26(3), 344–351
Intel Xeon Processor E5–2660 v. 3, 2020. https://ark.intel.com/content/www/us/en/ark/products/81706/intel-xeon-processor-e5-2660-v3-25m-cache-2-60-ghz.html
Isci, C.: Runtime power monitoring in high-end processors: methodology and empirical data. In: Proceedings of International Symposium on Microarchitecture (2003)
Bridges, R.A., et al.: Understanding GPU power: a survey of profiling, modeling, and simulation methods. ACM Comput. Surv. 49(3) (2016)
Ge, R., et al.: Powerpack: Energy profiling and analysis of high-performance systems and applications. IEEE Trans. Parall. Distrib. Syst. 21(5) (2010)
Bedard, D., et al.: Powermon: fine-grained and integrated power monitoring for commodity computer systems. In: Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon), pp. 479–484 (2010)
Libri, A., et al.: Dig: enabling out-of-band scalable high-resolution monitoring for data-center analytics, automation and control (extended). Clust. Comput. 24(4), 2723–2734 (2021)
Intelligent platform management interface (2020). https://www.intel.com/content/www/us/en/products/docs/servers/ipmi/ipmi-second-gen-interface-spec-v2-rev1-1.html
Singh, K., et al.: Real time power estimation and thread scheduling via performance counters. SIGARCH Comput. Archit. News 37(2), 46–55 (2009)
Sagi, M., et al.: Fine-grained power modeling of multicore processors using FFNNs. In: Orailoglu, A., Jung, M., Reichenbach, M. (eds.) Embedded Computer Systems: Architectures, Modeling, and Simulation, pp. 186–199. Springer, Cham (2020)
O’Brien, K., et al.: A survey of power and energy predictive models in HPC systems and applications. Comput. Surv. 50(3) (2017)
AMP. “amd opteron 6200series processors linux tuning guid (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Qi, X. et al. (2024). PowerDis: Fine-Grained Power Monitoring Through Power Disaggregation Model. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14490. Springer, Singapore. https://doi.org/10.1007/978-981-97-0859-8_20
Download citation
DOI: https://doi.org/10.1007/978-981-97-0859-8_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0858-1
Online ISBN: 978-981-97-0859-8
eBook Packages: Computer ScienceComputer Science (R0)