MAGIS: Memory Optimization via Coordinated Graph Transformation and Scheduling for DNN-Reference-Cited by-同舟云学术

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MAGIS: Memory Optimization via Coordinated Graph Transformation and Scheduling for DNN

Published:2024-04-27 Issue: Volume:2 Page:607-621
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3
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Short-container-title:

Author:

Chen Renze¹^ORCID,Ding Zijian²^ORCID,Zheng Size¹^ORCID,Zhang Chengrui¹^ORCID,Leng Jingwen³^ORCID,Liu Xuanzhe¹^ORCID,Liang Yun¹^ORCID

Affiliation:

1. Peking University, Beijing, China

2. University of California, Los Angeles, Los Angeles, USA

3. Shanghai Jiao Tong University, Beijing, China

Funder

National Natural Science Foundation of China (NSFC)

Publisher

ACM

Reference75 articles.

1. Megengine: A fast, scalable and easy-to-use deep learning framework. https://github.com/MegEngine/MegEngine, 2020.

2. torch.compiler - PyTorch 2.1 documentation. https://pytorch.org/docs/2.1/torch.compiler, 2023.

3. Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices;Ahn Byung Hoon;MLSys,2020

4. V Aho Alfred, S Lam Monica, and D Ullman Jeffrey. Compilers Principles, Techniques & Tools. pearson Education, 2007.

5. Olivier Beaumont, Lionel Eyraud-Dubois, and Alena Shilova. Efficient Combination of Rematerialization and Offloading for Training DNNs. In NIPS, volume 34, pages 23844--23857, 2021.

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