---
title: "Who cites Shivaram Venkataraman — 204 papers of our library"
description: "204 papers of the Elamur Research library cite the works of Shivaram Venkataraman, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/shivaram-venkataraman/cited-by/"
---
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# Who cites Shivaram Venkataraman

261 citations of their works from 204 papers of our library (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← Shivaram Venkataraman](https://md.research.elamur.ai/free/en/authors/shivaram-venkataraman/)

1. [**PyTorch Distributed: Experiences on Accelerating Data Parallel Training arXiv:2006.15704**Shen Li, Yanli Zhao, Rohan Varma, et al.*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-pytorch-distributed-experiences-accelerating-data/)
2. **[DataComp: In search of the next generation of multimodal datasets](https://md.research.elamur.ai/free/en/papers/gadre-2023-datacomp-search-next-generation-multimodal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, et al.*2023 · 224 citations*
3. [**Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads arXiv:1901.05758**Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, et al.*2019 · 179 citations*](https://md.research.elamur.ai/free/en/papers/jeon-2019-analysis-large-scale-multi-tenant/)
4. [**Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads arXiv:2008.09213**Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, et al.*2020 · 141 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2020-heterogeneity-aware-cluster-scheduling-policies/)
5. [**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/)
6. [**Themis: Fair and Efficient GPU Cluster Scheduling arXiv:1907.01484**Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, et al.*2019 · 87 citations*](https://md.research.elamur.ai/free/en/papers/mahajan-2019-themis-fair-efficient-gpu-cluster/)
7. [**Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training arXiv:2208.06102**Jie You, Jae-Won Chung, Mosharaf Chowdhury*2022 · 82 citations*](https://md.research.elamur.ai/free/en/papers/you-2022-zeus-understanding-optimizing-gpu-energy/)
8. **[Survey on Key Technologies for Large Language Model Pre-training Systems (大语言模型预训练系统关键技术综述)](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*

196 more citing papers of our library have no page here yet.

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