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Quality Assessment of GPU Power Profiling Mechanisms
Open accessAbstract
Accurate component-level power measurements are nowadays essential for the design and optimization of high-performance computing (HPC) systems and applications. Particularly, as more and more heterogeneous HPC systems are developed, the characterizations of GPU power profiles have become extremely crucial because, although GPUs provide exceptional performance, they do consume substantial amounts of power. Currently, there are various GPU power profiling mechanisms available; however, there is no standard way to assess the quality of such profiling schemes. To address this issue, in this paper, we develop an assessment methodology to rate the quality and performance of the profiling mechanism itself. Specifically, we present the assessments of four different GPU power profiling techniques: (i) Nvidia's NVML via Allinea MAP, (ii) Nvidia's NVML via direct reads, and (iii) Penguin Computing's PowerInsight (PI) via two vendor-provided drivers, and (iv) PowerInsight via Allinea MAP. In addition, we discuss the effects of moving-average filters to explain the slow variations of some of the measured power profiles. Based on our assessment, the GPU power profiling mechanism using PI device outperforms the other schemes by reliably measuring the ground-truth power profile generated by a GPU stress-test benchmark.
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“Errors highly depend on the specific chipset and even the workload”
From Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (2020) · cites this paper as Sen et al. (2018)
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Research works printed but not linked: 21
- 2009, Mali GPU Shader Development Studio User Guide
- 2015, AMD GPU Performance API - User Guide
- 0, Allinea MAP
- hsu, 2011, Power measurement for high performance computing: State of the art, Int Green Comput Conf Workshops (IGCC), 1
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- 10.1109/TPDS.2009.76 · DOI
- bedard, 2010, Pow-erMon: Fine-grained and integrated power monitoring for commodity computer systems, Proc IEEE Southeastcon, 479
- 10.1109/IGCC.2013.6604485 · DOI
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- 2012, Penguin Computing Releases New Power Monitoring Device
- 0, Performance application programming interface
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- mucci, 1999, PAPI: A portable interface to hardware performance counters, Proceedings of Department of Defense HPCMP Users Group Conference
- 0, NVIDIA Management Library (NVML)
- 0, Performance Analysis Tools
- 0, Oak Ridge Leadership Computing Facility Summit
- 10.1016/j.jcp.2012.11.031 · DOI
- 10.1145/2588768.2576783 · DOI
- 0, FIRESTARTER A processor Stress Test Utility
- 10.1109/IGCC.2013.6604507 · DOI
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Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020) · arXiv · doi:10.48550/arxiv.2002.05651 · Cited by 393
Sponge Examples: Energy-Latency Attacks on Neural Networks
Shumailov, I., Zhao, Y., Bates, D., Papernot, N., Mullins, R., & Anderson, R. (2021) · 2021 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp51992.2021.00024 · Cited by 125
Metadata
- Authors
- Sen, S., Imam, N., & Hsu, C.-H.
- Affiliations
- Oak Ridge National Laboratory (US)
- Venue
- 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 702-711
- ISSN
- 2164-7062 (ISSN-L)
- OpenAlex
- W2887659847
- Cited by
- 7
- References
- 0
- Citations by year
- 2024: 2 · 2023: 2 · 2021: 1 · 2020: 2
- FWCI
- 0.64
- Type
- conference paper
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Defense
- U.S. Department of Energy
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- power measurement accuracy, moving average filter, power consumption
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- The paper
- Sen, S., Imam, N., & Hsu, C.-H. (2018). Quality Assessment of GPU Power Profiling Mechanisms. 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). https://doi.org/10.1109/ipdpsw.2018.00113
