The DOI matches its Crossref record; 37 of the 39 references it cites were found in a registry or an index.
Efficient large-scale language model training on GPU clusters using megatron-LM
Open accessAbstract
Large language models have led to state-of-the-art accuracies across several tasks. However, training these models efficiently is challenging because: a) GPU memory capacity is limited, making it impossible to fit large models on even a multi-GPU server, and b) the number of compute operations required can result in unrealistically long training times. Consequently, new methods of model parallelism such as tensor and pipeline parallelism have been proposed. Unfortunately, naive usage of these methods leads to scaling issues at thousands of GPUs. In this paper, we show how tensor, pipeline, and data parallelism can be composed to scale to thousands of GPUs. We propose a novel interleaved pipelining schedule that can improve throughput by 10+% with memory footprint comparable to existing approaches. Our approach allows us to perform training iterations on a model with 1 trillion parameters at 502 petaFLOP/s on 3072 GPUs (per-GPU throughput of 52% of theoretical peak).
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“Current implementations have constraints on supported batch size per GPU, requiring it to be divisible by the number of pipeline stages.”
From The Llama 3 Herd of Models (2024) · cites this paper as Narayanan et al., 2021
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., & He, K. (2017) · arXiv · doi:10.48550/arxiv.1706.02677 · Cited by 2,294
PipeDream: generalized pipeline parallelism for DNN training
Narayanan, D., Harlap, A., Phanishayee, A., Seshadri, V., Devanur, N. R., Ganger, G., Gibbons, P. B., & Zaharia, M. (2019) · Symposium on Operating Systems Principles · doi:10.1145/3341301.3359646 · Cited by 1,362
Self-citation
Research works printed but not linked: 3
- Paras Jain , Ajay Jain , Aniruddha Nrusimha , Amir Gholami , Pieter Abbeel , Joseph Gonzalez , Kurt Keutzer , and Ion Stoica. Breaking the Memory Wall with Optimal Tensor Rematerialization. In Proceedings of Machine Learning and Systems 2020 , pages 497 -- 511 . 2020 . Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Joseph Gonzalez, Kurt Keutzer, and Ion Stoica. Breaking the Memory Wall with Optimal Tensor Rematerialization. In Proceedings of Machine Learning and Systems 2020, pages 497--511. 2020.
- Alec Radford , Karthik Narasimhan , Tim Salimans , and Ilya Sutskever . Improving Language Understanding by Generative Pre-Training , 2018 . Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving Language Understanding by Generative Pre-Training, 2018.
- Alec Radford , Jeffrey Wu , Rewon Child , David Luan , Dario Amodei , and Ilya Sutskever . Language Models are Unsupervised Multitask Learners . OpenAI Blog , 1 ( 8 ): 9 , 2019 . Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language Models are Unsupervised Multitask Learners. OpenAI Blog, 1(8):9, 2019.
Cited by
Sorted by citations
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
“Many other works aim to increase of the scale of models, while limiting communication overheads (Rajbhandari et al., 2020; Lepikhin et al., 2020; Li et al., 2020; Rasley et al., 2020; Rajbhandari et al., 2021; Ren et al., 2021; Narayanan et al., 2021a).”
BackgroundMethodInfluentialKimi K2.5: Visual Agentic Intelligence
Kimi Team, Bai, T., Bai, Y., Bao, Y., Cai, S. H., Cao, Y., Chai, Z., Charles, Y., Che, H. S., Chen, C., et al. (2026) · arXiv · doi:10.48550/arxiv.2602.02276 · Cited by 770
Holistic Evaluation of Language Models
Bommasani, R., Liang, P., & Lee, T. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15007 · Cited by 480
Pre-Trained Language Models and Their Applications
Wang, H., Li, J., Wu, H. R., Hovy, E. H., & Sun, Y. (2022) · Engineering · doi:10.1016/j.eng.2022.04.024 · Cited by 435
Siriwardhana, S., Weerasekera, R., Wen, E., Kaluarachchi, T., Rana, R., & Nanayakkara, S. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00530 · Cited by 337
Metadata
- Authors
- Narayanan, D., Shoeybi, M., Casper, J., LeGresley, P., Patwary, M., Korthikanti, V., Vainbrand, D., Kashinkunti, P., Bernauer, J., Catanzaro, B., Phanishayee, A., & Zaharia, M.
- Affiliations
- NVIDIA and Microsoft Research
- Nvidia (United Kingdom) (GB)
- Microsoft (United States) (US)
- Stanford University (US)
- ORCID
- Venue
- Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
- Publisher
- ACM
- Published
- 2021-11-13
- Pages
- 1-15
- ISSN
- 2167-4337 (ISSN-L)
- OpenAlex
- W3187255235
- Cited by
- 639
- References
- 39
- Citations by year
- 2026: 151 · 2025: 211 · 2024: 152 · 2023: 90 · 2022: 33 · 2021: 2
- FWCI
- 50.34
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference for High Performance Computing, Networking, Storage and Analysis (SC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (DGE-165651 8, 1651570, 1656518, CAREER)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language model training, GPU clusters, tensor parallelism, pipeline parallelism, data parallelism, memory efficiency, training throughput
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