--- title: "Who cites Demystifying Parallel and — 72 citing papers" description: "72 papers of the Elamur Research library cite «Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis»…" canonical: "https://research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Demystifying Parallel and Distributed Deep…](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/) / cited by # Who cites «Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis» 72 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis»](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/) 1. [**ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning SC 2021**Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, et al.*2021 · 254 citations*](https://md.research.elamur.ai/free/en/papers/rajbhandari-2021-zero-infinity-breaking-gpu-memory/) 2. **[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* 70 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/) — Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis