--- title: "Who cites cuDNN: Efficient Primitives for Deep Learning" description: "297 papers of the Elamur Research library cite «cuDNN: Efficient Primitives for Deep Learning», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/chetlur-2014-cudnn-efficient-primitives-deep-learning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chetlur-2014-cudnn-efficient-primitives-deep-learning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [cuDNN: Efficient Primitives for Deep Learning](https://md.research.elamur.ai/free/en/papers/chetlur-2014-cudnn-efficient-primitives-deep-learning/) / cited by # Who cites «cuDNN: Efficient Primitives for Deep Learning» 297 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · [← back to «cuDNN: Efficient Primitives for Deep Learning»](https://md.research.elamur.ai/free/en/papers/chetlur-2014-cudnn-efficient-primitives-deep-learning/) 1. [**PyTorch: An Imperative Style, High-Performance Deep Learning Library arXiv:1912.01703**Adam Paszke, Sam Gross, Francisco Massa, et al.*2019 · 18,645 citations*](https://md.research.elamur.ai/free/en/papers/paszke-2019-pytorch-imperative-style-high-performance/) 2. [**TensorFlow: A system for large-scale machine learning arXiv:1605.08695**Martin Abadi, Paul Barham, Jianmin Chen, et al.*2016 · 12,711 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-system-large-scale-machine/) 3. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems arXiv:1603.04467**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,512 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/) 4. [**Deep Speech 2: End-to-End Speech Recognition in English and Mandarin arXiv:1512.02595**Dario Amodei, Rishita Anubhai, Eric Battenberg, et al.*2015 · 2,341 citations*](https://md.research.elamur.ai/free/en/papers/amodei-2015-deep-speech-2-end-end/) 5. [**Deep Speech: Scaling up end-to-end speech recognition arXiv:1412.5567**Awni Hannun, Carl Case, Jared Casper, et al.*2014 · 1,600 citations*](https://md.research.elamur.ai/free/en/papers/hannun-2014-deep-speech-scaling-up-end/) 6. **[PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) [ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-architectural-support-for-programming-languages/)**Jason Ansel, Edward Yang, Horace He, et al.*2024 · 946 citations* 7. [**Learning Structured Sparsity in Deep Neural Networks arXiv:1608.03665**Wei Wen, Chunpeng Wu, Yandan Wang, et al.*2016 · 530 citations*](https://md.research.elamur.ai/free/en/papers/wen-2016-learning-structured-sparsity-deep-neural/) 8. [**NVIDIA Tensor Core Programmability, Performance & Precision IPDPSW 2018 (arXiv)**Stefano Markidis, Steven Wei Der Chien, Erwin Laure, et al.*2018 · 465 citations*](https://md.research.elamur.ai/free/en/papers/markidis-2018-nvidia-tensor-core-programmability-performance/) 9. [**Beyond Data and Model Parallelism for Deep Neural Networks SysML 2019**Zhihao Jia, Matei Zaharia, Alex Aiken*2018 · 280 citations*](https://md.research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/) 10. [**Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis ACM Computing Surveys 2019**Tal Ben-Nun, Torsten Hoefler*2018 · 275 citations*](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/) 11. **[Sparse GPU Kernels for Deep Learning](https://md.research.elamur.ai/free/en/papers/gale-2020-sparse-gpu-kernels-deep-learning/) [2012 International Conference for High Performance Computing, Networking, Storage and Analysis](https://md.research.elamur.ai/free/en/venues/2012-international-conference-for-high-performance-computing-networking-storage/)**Trevor Gale, Matei Zaharia, Cliff Young, et al.*2020 · 259 citations* 12. [**Efficient Processing of Deep Neural Networks: A Tutorial and Survey Proceedings of the IEEE 2017**Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, et al.*2017 · 146 citations*](https://md.research.elamur.ai/free/en/papers/sze-2017-efficient-processing-deep-neural-networks/) 13. **[Direct Acoustics-to-Word Models for English Conversational Speech Recognition](https://md.research.elamur.ai/free/en/papers/audhkhasi-2017-direct-acoustics-word-models-english/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Kartik Audhkhasi, Bhuvana Ramabhadran, George Saon, et al.*2017 · 122 citations* 14. [**Vidur: A Large-Scale Simulation Framework For LLM Inference arXiv:2405.05465**Amey Agrawal, Nitin Kedia, Jayashree Mohan, et al.*2024 · 90 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2024-vidur-large-scale-simulation-framework/) 15. [**vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design MICRO 2016**Minsoo Rhu, Natalia Gimelshein, Jason Clemons, et al.*2016 · 81 citations*](https://md.research.elamur.ai/free/en/papers/rhu-2016-vdnn-virtualized-deep-neural-networks/) 16. [**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/) 281 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/chetlur-2014-cudnn-efficient-primitives-deep-learning/) — cuDNN: Efficient Primitives for Deep Learning