--- title: "Who cites Beyond Data and Model — 140 citing papers" description: "140 papers of the Elamur Research library cite «Beyond Data and Model Parallelism for Deep Neural Networks», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Beyond Data and Model Parallelism for Deep Neural…](https://md.research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/) / cited by # Who cites «Beyond Data and Model Parallelism for Deep Neural Networks» 140 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · [← back to «Beyond Data and Model Parallelism for Deep Neural Networks»](https://md.research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/) 1. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) 2. [**Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning OSDI 2022**Lianmin Zheng, Zhuohan Li, Hao Zhang, et al.*2022 · 279 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/) 3. [**FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance arXiv:2305.05176**Lingjiao Chen, Matei Zaharia, James Zou*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-frugalgpt-use-large-language-models/) 4. [**Mesh-TensorFlow: Deep Learning for Supercomputers arXiv:1811.02084**Noam Shazeer, Youlong Cheng, Niki Parmar, et al.*2018 · 203 citations*](https://md.research.elamur.ai/free/en/papers/shazeer-2018-mesh-tensorflow-deep-learning-supercomputers/) 5. [**PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation arXiv:2104.12369**Wei Zeng, Xiaozhe Ren, Teng Su, et al.*2021 · 161 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2021-pangu-alpha-large-scale/) 6. **[Memory-Efficient Pipeline-Parallel DNN Training](https://md.research.elamur.ai/free/en/papers/narayanan-2020-memory-efficient-pipeline-parallel-dnn/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, et al.*2020 · 153 citations* 7. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 citations* 8. [**DAPPLE: A Pipelined Data Parallel Approach for Training Large Models arXiv:2007.01045**Shiqing Fan, Yi Rong, Chen Meng, et al.*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fan-2020-dapple-pipelined-data-parallel-approach/) 9. [**Accelerating Distributed MoE Training and Inference with Lina arXiv:2210.17223**Jiamin Li, Yimin Jiang, Yibo Zhu, et al.*2022 · 92 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-accelerating-distributed-moe-training-inference/) 10. [**TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions SOSP 2019**Zhihao Jia, Oded Padon, James Thomas, et al.*2019 · 72 citations*](https://md.research.elamur.ai/free/en/papers/jia-2019-taso-optimizing-deep-learning-computation/) 11. **[High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing](https://md.research.elamur.ai/free/en/papers/wang-2026-high-throughput-memory-efficient-zeroth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Liangyu Wang, Huanyi Xie, Di Wang*2026* 129 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/) — Beyond Data and Model Parallelism for Deep Neural Networks