Towards Energy Monitoring in Visual Processing Pipelines

Metadata

Publisher: SMPTE — Pasadena, CA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 00, pp. 1–24
Abstract: With over 80% of global internet traffic attributed to video content, and tentpole movies averaging 2,840 tonnes of CO2 emissions, the media industry faces urgent sustainability challenges that will intensify with the increased adoption of AI in production workflows. The industry is embracing techniques such as Virtual Production (VP), and its wider adoption is expected to reduce the energy demands and carbon footprint. However, there are no existing standards or tools available to developers of visual processing algorithms, techniques and systems to assess the energy footprint of existing workflows in post-production and VP, or to guide the development of new algorithms and tools that are optimised for energy consumption. The main contribution of this work is to outline frameworks for monitoring the energy consumption of existing video processing pipelines using a set of software and hardware tools, and thus establish a standardised method to perform energy consumption measurements/profiling at runtime. Two different approaches for energy monitoring are presented to understand the current power requirements of standard VP hardware. The first approach uses deployable out-of-band monitoring interfaces for real-time monitoring and capacity planning. The second approach builds on profiling techniques to characterise the accuracy of on-device and on-chip power measurements, developing an invasive scheme to characterise the energy costs of mapping existing VP tasks to specific resources (CPU or GPU). Together, they enable runtime monitoring and granular characterisation to aid with energy-aware development and deployment of post-production and VP workflows.
Publication Date: 2025-10-13
ISBN: Electronic: 978-1-61482-966-9
Author(s): Hareesh Veekanchery
Helios Lyons
Philip Coulam-Jones
Shreejith Shanker
Keyword(s): Energy Efficiency, Virtual Production, Energy Monitoring
Copyright: © 2025 SMPTE

Bibliographic Reference(s)

Albert, British Film Institute and Arup, “A screen new deal: A route map to sustainable film production,” Tech Report, wearealbert.org, Mar. 2021. [Online]. Available: https://wearealbert.org/wp-content/uploads/2021/03/Screen-New-Deal-Report-1.pdf. [Accessed 25 Aug. 2025]. EXTERNAL
V. Helzle, S. S. and T. J., “Green Screens, Green Pixels and Green Shooting,” in ACM SIGGRAPH Talks, Feb. 2022. [Online]. Available: https://animationsinstitut.de/files/public/images/04-forschung/Publications/FAbw_VirtualProductionSustainabilityReport_2_22_En.pdf EXTERNAL
D. Keeney, “Virtual Production's Role in Carbon Reduction and Net Zero Production in the Screen Industries,” Future Observatory Research Report, 2023. [Online]. Available: https://futureobservatory.org/files/dcmsreports/futureobservatory_culturalpolicyreport_studioulster.pdf. [Accessed 25 Aug. 2025]. EXTERNAL
M. Jay, V. Ostapenco, L. Lefèvre, D. Trystram, A.-C. Orgerie and B. Fichel, “An experimental comparison of software-based power meters: focus on CPU and GPU,” in 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 106–118, 2023. EXTERNAL
S. Song, C. Su, B. Rountree and K. W. Cameron, “A simplified and accurate model of power-performance efficiency on emergent GPU architectures,” in IEEE 27th International Symposium on Parallel and Distributed Processing, pp. 673–686, 2013. EXTERNAL
R. A. Bridges, N. Imam and T. M. Mintz, “Understanding GPU power: A survey of profiling, modeling, and simulation methods,” ACM Comput. Surv., vol. 49, no. 3, pp. 1–27, 2016. EXTERNAL
NVIDIA, “System Management Interface” System Management Interface program [Online] Available: https://docs.nvidia.com/deploy/nvidia-smi/index.html EXTERNAL
Kavanagh, Richard, Django Armstrong, and Karim Djemame. “Accuracy of energy model calibration with IPMI.” 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. EXTERNAL
Z. Yang, K. Adamek, and W. Armour, “Part-time power measurements: NVIDIA-smi's lack of attention,” arXiv preprint arXiv:2312.02741, 2023. EXTERNAL
Z. Yang, K. Adamek, and W. Armour, “Accurate and convenient energy measurements for GPUs: A detailed study of NVIDIA GPU's built-in power sensor,” in Proc. SC24: Int. Conf. High Performance Computing, Networking, Storage and Analysis, 2024, pp. 1–17. EXTERNAL
Yoshida, K., Shinobu, M., Yamaki, H., & Honda, H. (2024). Analyzing the impact of CUDA versions on GPU applications. Parallel Computing, 120. EXTERNAL
X. Ma, M. Dong, L. Zhong, and Z. Deng, “Statistical Power Consumption Analysis and Modeling for GPU-based Computing,” in *Proceedings of the 2nd Workshop on Power Aware Computing and Systems (HotPower'09)*,2009. EXTERNAL
Q. Zhao, H. Yang, Z. Luan, and D. Qian, “POIGEM: A programming-oriented instruction level GPU energy model for CUDA program,” in *Proc. 13th Int. Conf. Algorithms and Architectures for Parallel Processing (ICA3PP)*, Springer, 2013, pp. 129–142. EXTERNAL
W. Choi, D. Bedard, R. Fowler, and R. Vuduc, “A roofline model of energy,” in Proc. 27th IEEE Int. Symp. Parallel and Distributed Processing (IPDPS), Boston, MA, USA, May 2013, pp. 661–672. EXTERNAL
D. Bedard, M. Y. Lim, R. Fowler, and A. Porterfield, “Powermon: Fine-grained and integrated power monitoring for commodity computer systems,” in Proc. IEEE SoutheastCon, Concord, NC, USA, Mar. 2010, pp. 479–484. EXTERNAL
S. Sen, N. Imam, and C.-H. Hsu, “Quality assessment of GPU power profiling mechanisms,” in Proc. IEEE Int. Parallel and Distributed Processing Symp. Workshops (IPDPSW), Vancouver, BC, Canada, May 2018, pp. 702–711. EXTERNAL
J. H. Laros, P. Pokorny, and D. DeBonis, “PowerInsight: A commodity power measurement capability,” in Proc. Int. Green Computing Conf. (IGCC), Arlington, VA, USA, Jun. 2013, pp. 1–6. EXTERNAL
K. N. Han, M. Hirki, T. Niemi, J. K. Nurminen, and Z. Ou, “RAPL in action: Experiences in using RAPL for power measurements,” ACM Transactions on Modeling and Performance Evaluation of Computing Systems, vol. 3, no. 2, pp. 1–26, 2018. EXTERNAL
Intel Corporation, “Get Started with Energy Analysis,” Intel® SoC Watch Get Started Guide (Version 2023.1), Mar. 31, 2023. [Online]. EXTERNAL
J. M. Paniego, S. Gallo, M. Pi Puig, F. Chichizola, L. De Giusti, and J. Balladini, “Analysis of RAPL Energy Prediction Accuracy in a Matrix Multiplication Application on Shared Memory,” in Communications in Computer and Information Science: Computer Science - CACIC 2017, vol. 790, Springer, Cham, 2018, pp. 37–46, doi: 10.1007/978-3-319-75214-3_4. EXTERNAL
CodeCarbon, “Track and reduce CO2 emissions from your computing,” CodeCarbon - Home Page, [Online]. Available: https://codecarbon.io/ EXTERNAL
P. Henderson, J. Hu, J. Romoff, E. Brunskill, D. Jurafsky, and J. Pineau, “Towards the systematic reporting of the energy and carbon footprints of machine learning,” Journal of Machine Learning Research, vol. 21, no. 248, pp. 1–43, 2020. EXTERNAL
D. Brooks, V. Tiwari, and M. Martonosi, “Wattch: A framework for architectural-level power analysis and optimisations,” in Proceedings of the 27th International Symposium on Computer Architecture (ISCA '00), 2000, p. 83. EXTERNAL
S. Li, J.-H. Ahn, R. D. Strong, J. B. Brockman, D. M. Tullsen, and N. P. Jouppi, “McPAT: An integrated power, area, and timing modeling framework for multicore and manycore architectures,” in Proc. 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), New York, NY, USA, Dec. 2009, pp. 469–480. EXTERNAL
J. Leng, T. Hetherington, A. EITantawy, S. Gilani, N. S. Kim, T. M. Aamodt, and V. J. Reddi, “GPUWattch: Enabling energy optimisations in GPGPUs,” in Proc. 40th Annual Int. Symp. Computer Architecture (ISCA '13), 2013, pp. 487–498, doi: 10.1145/2485922.2485964. EXTERNAL
P. Pathania, R. Mehra, V. S. Sharma, V. Kaulgud, S. Podder, and A. P. Burden, “Esave: Estimating server and virtual machine energy,” in Proc. 37th IEEE/ACM International Conference on Automated Software Engineering (ASE), Luxembourg, Sept. 2023, pp. 1–12. Association for Computing Machinery. EXTERNAL
X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,” in Proc. 34th Annual International Symposium on Computer Architecture (ISCA), San Diego, CA, USA, Jun. 2007, pp. 13–23. Association for Computing Machinery. EXTERNAL
L. D. Gray, A. Kumar, and H. H. Li, “Workload characterization of the SPECpower_ssj2008 benchmark,” in Performance Evaluation: Metrics, Models and Benchmarks, S. Kounev, I. Gorton, and K. Sachs,Eds. Berlin, Heidelberg: Springer, 2008, pp. 262–282. EXTERNAL
H. Nagasaka, N. Maruyama, A. Nukada, T. Endo, and S. Matsuoka, “Statistical power modeling of GPU kernels using performance counters,” in Proc. Int. Green Computing Conf. (IGCC), Chicago, IL, USA, Aug. 2010, pp. 1–8. EXTERNAL
G. K. Alavani, J. Desai, S. Saha, and S. Sarkar, “Program Analysis and Machine Learning-based Approach to Predict Power Consumption of CUDA Kernel,” ACM Transactions on Modeling and Performance Evaluation of Computing Systems, vol. 8, no. 4, pp.1–24, Dec. 2023, doi: 10.1145/3603533. EXTERNAL
D. Li, H. You, W. Jiang, X. Chen, C. Zeng, and X. Sun, “Enhanced power saving schemes for extended reality,” in Proceedings of the IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Sep. 2021, pp. 1–6. EXTERNAL
Barco, “Energy efficiency of different display technologies in control rooms,” Barco, 2023. Available:https://www.barco.com/en/inspiration/news-insights/2023-05-24-energy-efficiency-of-different-display-technologies-in-control-rooms EXTERNAL
P. Yuliantoro, S. Romadhona, and D. O. Ayuninda, “Monitoring Power Consumption on LED Using LED Matrix P10 Panel,” Journal of Applied Information and Communication Technologies (JAICT), vol. 8, no. 1, p. 146, Mar. 2023, doi: 10.32497/jaict.v8i1.4072. EXTERNAL
R. Ganber, *NUKE 101: Professional Compositing and Visual Effects*, New York, USA: Peachpit Press, 2014. EXTERNAL
J. Reinders, *VTune Performance Analyzer Essentials*, vol. 9, Santa Clara, CA, USA:Intel Press, 2005. EXTERNAL
Disguise Systems Ltd., “GX3 Specification Sheet,” 2023. Available: https://www.disguise.one/media/8371/spec_sheet_gx3.pdf EXTERNAL
H. Lyons, “System Management Controller,” Disguise User Guide, 2025. https://help.disguise.one/hardware/smc/smc-overview (accessed May 01, 2025). EXTERNAL
Power Management Bus (PMBus®) Specification Rev. 1.4. System Management Interface Forum, Inc., 2024. Accessed: May 01, 2025. [Online]. Available: pmbus.org EXTERNAL
System Management Bus (SMBus) Specification, Version 3.3.1. System Management Interface Forum, Inc., 2024. Accessed: May 01, 2025. [Online]. Available: https://smbus.org/specs/SMBus_3_3_1_20241020.pdf EXTERNAL
ASPEED AST2600 BMC Users Guide X12/H12. 980 Rock Ave. San Jose, CA 95131 U.S.A.: Super Micro Computer, Inc., 2022. Accessed: May 01, 2025. [Online]. Available: https://www.supermicro.com/manuals/other/BMC_Users_Guide_X12_H12.pdf EXTERNAL
D. Laurie, ipmitool(1)1.8.11–5.2011. Available: https://manpages.debian.org/wheezy/ipmitool/ipmitool.1.en.html EXTERNAL
H. Lyons, “System Management Controller (SMC) Application Programming Interface,” disguise.one, 2025. https://developer.disguise.one/smc/introduction/ (accessed May 01, 2025). EXTERNAL
Disguise Systems Ltd., “VX4+ Product Specification,” 2022. Available: https://www.disguise.one/en/products/vx-4plus EXTERNAL
Infiled, “DB Series V2,” Jan. 2023. Accessed: May 01, 2025. [Online]. Available: https://www.infiled.com/wp-content/uploads/2024/01/db-series.pdf EXTERNAL
Brompton Technology Ltd., “TESSERA S8 LED Processor,” Sep. 2024. Accessed: May 01, 2025. [Online]. Available: https://www.bromptontech.com/wp-content/uploads/2024/09/Brompton-S8-Data-Sheet-Sep2024-EN.pdf EXTERNAL
International Electrotechnical Commission, Multimedia systems and equipment - Colour measurement and management - Part 2–1: Colour management - Default RGB colour space - sRGB. 3, rue de Varembé, PO Box 131, CH-1211 Geneva 20, Switzerland: IEC, 1999. Available: https://cdn.standards.iteh.ai/samples/10795/ae461684569b40bbbb2d9a22b1047f05/1EC-61966-2-1-1999-AMD1-2003.pdf EXTERNAL
Future Observatory, “Virtual Production and the Carbon Footprint of the Screen Industries,” 2023. Available: https://futureobservatory.org/files/dcmsreports/futureobservatory_culturalpolicyreport_studioulster.pdf EXTERNAL

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Hareesh Veekanchery, Helios Lyons, Philip Coulam-Jones, and Shreejith Shanker; Towards Energy Monitoring in Visual Processing Pipelines, MTS 2025, Article 29 (pp. 1 to 24); SMPTE, 2025, ISBN: 978-1-61482-966-9

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Hareesh Veekanchery, Helios Lyons, Philip Coulam-Jones, and Shreejith Shanker; <cite id="bib-978-1-61482-966-9-29">Towards Energy Monitoring in Visual Processing Pipelines</cite>, MTS 2025, Article 29 (pp. 1 to 24); SMPTE, 2025, ISBN: 978-1-61482-966-9
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