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Recent breakthroughs of large language models (LLMs) have exhibited superior capability across major industries and stimulated multi-hundred-billion-dollar investment in AI-centric data centers in the next 3-5 years.
B. Kirby and M. Milligan, “Method and case study for estimating the ramping capability of a control area or balancing authority and implications for moderate or high wind penetration,” National Renewable Energy Lab., Golden, CO (US), Tech. Rep., 2005
2005
Earlier work this paper cites.
D. Salomonsson, L. Soder, and A. Sannino, “An adaptive control system for a dc microgrid for data centers,” in 2007 IEEE Industry Applications Annual Meeting . IEEE, 2007, pp. 2414–2421
2007
Earlier work this paper cites.
P. Wang, P. McCluskey, and A. Bar-Cohen, “Two-phase liquid cooling for thermal management of igbt power electronic module,” Journal of Electronic Packaging , vol. 135, no. 2, p. 021001, 2013
2013
Earlier work this paper cites.
F. Kong and X. Liu, “A survey on green-energy-aware power management for datacenters,” ACM Computing Surveys (CSUR) , vol. 47, no. 2, pp. 1–38, 2014
2014
Earlier work this paper cites.
E. Oró, V. Depoorter, A. Garcia, and J. Salom, “Energy efficiency and renewable energy integration in data centres. strategies and modelling review,” Renewable and Sustainable Energy Reviews , vol. 42, pp. 429–445, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1364032114008600
2015
Earlier work this paper cites.
J. Zhang, F. R. Yu, S. Wang, T. Huang, Z. Liu, and Y. Liu, “Load balancing in data center networks: A survey,” IEEE Communications Surveys & Tutorials , vol. 20, no. 3, pp. 2324–2352, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
Z. Jia, M. Zaharia, and A. Aiken, “Beyond data and model parallelism for deep neural networks.” Proceedings of Machine Learning and Systems , vol. 1, pp. 1–13, 2019
2019
Earlier work this paper cites.
S. Kwon, “Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations,” Applied Energy , vol. 276, p. 115424, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0306261920309363
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
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
2020
Earlier work this paper cites.
S. McQueen, J. Stanford, S. Satyapal, E. Miller, N. Stetson, D. Papageorgopoulos, N. Rustagi, V. Arjona, J. Adams, K. Randolph et al. , “Department of energy hydrogen program plan,” US Department of Energy (USDOE), Washington DC (United States), Tech. Rep., 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
M. Lui, Y. Yetim, Ö. Özkan, Z. Zhao, S.-Y. Tsai, C.-J. Wu, and M. Hempstead, “Understanding capacity-driven scale-out neural recommendation inference,” in 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) . IEEE, 2021, pp. 162–171
2021
Earlier work this paper cites.
A. Radovanovic, B. Chen, S. Talukdar, B. Roy, A. Duarte, and M. Shahbazi, “Power modeling for effective datacenter planning and compute management,” IEEE Transactions on Smart Grid , vol. 13, no. 2, pp. 1611–1621, 2021
2021
Earlier work this paper cites.
S. Samsi, M. L. Weiss, D. Bestor, B. Li, M. Jones, A. Reuther, D. Edelman, W. Arcand, C. Byun, J. Holodnack et al. , “The mit supercloud dataset,” in 2021 IEEE High Performance Extreme Computing Conference (HPEC) . IEEE, 2021, pp. 1–8
2021
Earlier work this paper cites.
L. H. Kaack, P. L. Donti, E. Strubell, G. Kamiya, F. Creutzig, and D. Rolnick, “Aligning artificial intelligence with climate change mitigation,” Nature Climate Change , vol. 12, no. 6, pp. 518–527, 2022
2022
Earlier work this paper cites.
J. Sevilla, L. Heim, A. Ho, T. Besiroglu, M. Hobbhahn, and P. Villalobos, “Compute trends across three eras of machine learning,” in 2022 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2022, pp. 1–8
2022
Earlier work this paper cites.
B. Li, R. Arora, S. Samsi, T. Patel, W. Arcand, D. Bestor, C. Byun, R. B. Roy, B. Bergeron, J. Holodnak et al. , “Ai-enabling workloads on large-scale gpu-accelerated system: Characterization, opportunities, and implications,” in 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA) . IEEE, 2022, pp. 1224–1237
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
C.-J. Wu, R. Raghavendra, U. Gupta, B. Acun, N. Ardalani, K. Maeng, G. Chang, F. Aga, J. Huang, C. Bai et al. , “Sustainable ai: Environmental implications, challenges and opportunities,” Proceedings of Machine Learning and Systems , vol. 4, pp. 795–813, 2022
2022
Cited alongside, same era.
Y. R. Li, F. Nejabatkhah, and H. Tian, Smart hybrid AC/DC microgrids: power management, energy management, and power quality control . John Wiley & Sons, 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
ML Energy, “Ml energy leaderboard,” https://ml.energy/leaderboard/?__theme=light , 2024, accessed: 2024-09-07
2024
Closest in time.
A. Llama Team, “The llama 3 herd of models,” July 2024, a detailed contributor list can be found in the appendix of this paper. [Online]. Available: https://llama.meta.com/
2024
Closest in time.
A. Elmeleegy, S. Raj, B. Slechta, and V. Mehta, “Demystifying ai inference deployments for trillion parameter large language models,” https://developer.nvidia.com/blog/demystifying-ai-inference-deployments-for-trillion-parameter-large-language-models/ , June 2024, technical Blog, Data Center / Cloud, NVIDIA. Accessed: 2024-09-07
2024
Closest in time.
P. Patel, E. Choukse, C. Zhang, Í. Goiri, B. Warrier, N. Mahalingam, and R. Bianchini, “Characterizing power management opportunities for llms in the cloud,” in Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 , 2024, pp. 207–222
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. de Vries, “The growing energy footprint of artificial intelligence,” Joule , vol. 7, no. 10, pp. 2191–2194, 2023
2023
Cited alongside, same era.
V. Avelar, P. Donovan, P. Lin, W. Torell, and M. A. T. Arango, “The ai disruption: Challenges and guidance for data center design,” Schneider Electric [Online] , 2023
2023
Cited alongside, same era.
A. S. Luccioni, S. Viguier, and A.-L. Ligozat, “Estimating the carbon footprint of bloom, a 176b parameter language model,” Journal of Machine Learning Research , vol. 24, no. 253, pp. 1–15, 2023
2023
Cited alongside, same era.
S. Samsi, D. Zhao, J. McDonald, B. Li, A. Michaleas, M. Jones, W. Bergeron, J. Kepner, D. Tiwari, and V. Gadepally, “From words to watts: Benchmarking the energy costs of large language model inference,” in 2023 IEEE High Performance Extreme Computing Conference (HPEC) , 2023, pp. 1–9
2023
Cited alongside, same era.
G. 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, 2023
2023
Cited alongside, same era.
J. Cao, R. Sen, M. Interlandi, J. Arulraj, and H. Kim, “Gpu database systems characterization and optimization,” Proceedings of the VLDB Endowment , vol. 17, no. 3, pp. 441–454, 2023
2023
Cited alongside, same era.
2024
Closest in time.
Q. Hu, Z. Ye, Z. Wang, G. Wang, M. Zhang, Q. Chen, P. Sun, D. Lin, X. Wang, Y. Luo et al. , “Characterization of large language model development in the datacenter,” in 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24) , 2024, pp. 709–729
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Google, “Data center efficiency,” https://www.google.com/about/datacenters/efficiency , 2024, accessed: 2024-09-07
2024
Closest in time.
H. Face, “Gpt-2 model documentation,” https://huggingface.co/docs/transformers/model_doc/gpt2 , 2024, accessed: 2024-09-07
2024
Closest in time.
A. Karpathy, “nanogpt: A tiny gpt implementation in pytorch,” https://github.com/karpathy/nanoGPT , 2024, accessed: 2024-09-07
2024
Closest in time.
D. Jia, “A guide to implementing and training generative pre-trained transformers (gpt) in jax on amd gpus,” https://rocm.blogs.amd.com/artificial-intelligence/nanoGPT-JAX/README.html , July 2024, accessed: 2024-09-07
2024
Closest in time.
S. Song, J. Adeem, and M. Arseny, “Mamba on amd gpus with rocm,” https://rocm.blogs.amd.com/artificial-intelligence/mamba/README.html , June 2024, accessed: 2024-09-07
2024
Closest in time.
2024
Closest in time.
M. Mughees, Y. Li, and R. Y. Li, “From c.elegans to liquid neural networks: A robust wind power multi-time scale prediction framework,” Aug. 2024. [Online]. Available: http://dx.doi.org/10.36227/techrxiv.172469941.17523365/v1
2024
Closest in time.
S. Spaces, “Mamba: An efficient transformer model,” https://github.com/state-spaces/mamba , 2024, accessed: 2024-09-07
2024
Closest in time.
EleutherAI, “Gpt-neo: An implementation of gpt architecture by eleutherai,” https://www.eleuther.ai/artifacts/gpt-neo , 2024, accessed: 2024-09-07
2024
Closest in time.
NVIDIA, “Nvidia volta v100 datasheet,” https://images.nvidia.com/content/technologies/volta/pdf/volta-v100-datasheet-update-us-1165301-r5.pdf , 2024, accessed: 2024-09-07
2024
Closest in time.
——, “Nvidia a40 datasheet,” https://images.nvidia.com/content/Solutions/data-center/a40/nvidia-a40-datasheet.pdf , 2024, accessed: 2024-09-07
2024
Closest in time.
T. City, “Geforce rtx 4090 vs radeon rx 7900 xtx comparison,” https://technical.city/en/video/GeForce-RTX-4090-vs-Radeon-RX-7900-XTX , 2024, accessed: 2024-09-07
2024
Closest in time.