The Optimization of Model Parallelization Strategies for Multi-GPU Training
Author:
Affiliation:
1. College of Information Science and Electronic, Engineering Zhejiang University,Hangzhou,PRC
2. China Tower Corporation Limited,Hangzhou,PRC
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9685019/9685006/09685964.pdf?arnumber=9685964
Reference22 articles.
1. Exploring hidden dimensions in parallelizing convolutional neural networks;jia;CoRR abs/1802 04924,2018
2. Legion: Expressing locality and independence with logical regions
3. Experiments on parallel training of deep neural network using model averaging;su;ArXiv Preprint,2015
4. Accurate, large minibatch SGD: training imagenet in 1 hour;goyal;CoRR abs/1706 02677,2017
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1. CSIMD: Cross-Search Algorithm with Improved Multi-dimensional Dichotomy for Micro-Batch-Based Pipeline Parallel Training in DNN;Lecture Notes in Computer Science;2024
2. Modoru: Clos nanosecond optical switching for distributed deep training [Invited];Journal of Optical Communications and Networking;2023-12-13
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