mCAP: Memory-Centric Partitioning for Large-Scale Pipeline-Parallel DNN Training-Reference-Cited by-同舟云学术

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mCAP: Memory-Centric Partitioning for Large-Scale Pipeline-Parallel DNN Training

Published:2022 Issue: Volume: Page:155-170
ISSN:0302-9743
Container-title:Euro-Par 2022: Parallel Processing
language:
Short-container-title:

Author:

Dreuning Henk,Bal Henri E.,Nieuwpoort Rob V. van

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-12597-3_10

Reference26 articles.

1. Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: OSDI, pp. 265–283 (2016)

2. Awan, A.A., et al.: OC-DNN: exploiting advanced unified memory capabilities in CUDA 9 and Volta GPUs for out-of-core DNN training. In: HiPC, pp. 143–152. IEEE (2018)

3. Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255. IEEE (2009)

4. Dreuning, H., et al.: Artifact and instructions to generate experimental results for Euro-Par 2022 paper: mCAP: Memory-Centric Partitioning for Large-Scale Pipeline-Parallel DNN Training (2022). https://doi.org/10.6084/m9.figshare.20000960

5. Fan, S., et al.: DAPPLE: a pipelined data parallel approach for training large models. In: PPoPP, pp. 431–445 (2021)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CAPSlog: Scalable Memory-Centric Partitioning for Pipeline Parallelism;2024 32nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP);2024-03-20

2. CAPTURE: Memory-Centric Partitioning for Distributed DNN Training with Hybrid Parallelism;2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC);2023-12-18

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