Towards Low-Overhead Resilience for Data Parallel Deep Learning-Reference-Cited by-同舟云学术

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Towards Low-Overhead Resilience for Data Parallel Deep Learning

Published:2022-05 Issue: Volume: Page:
ISSN:
Container-title:2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
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Author:

Nicolae Bogdan¹,Hobson Tanner²,Yildiz Orcun¹,Peterka Tom¹,Morozov Dmitry³

Affiliation:

1. Argonne National Laboratory,USA

2. University of Knoxville,Tennessee,USA

3. Lawrence Berkeley National Laboratory,USA

Funder

U.S. Department of Energy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9825911/9825913/09825929.pdf?arnumber=9825929

Reference22 articles.

1. Leveraging Naturally Distributed Data Redundancy to Reduce Collective I/O Replication Overhead

2. DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models

3. DeepClone: Lightweight State Replication of Deep Learning Models for Data Parallel Training

4. Checkfreq: Frequent, fine-grained DNN checkpointing;mohan;FAST'21 19th USENIX Confer-ence on File and Storage Technologies,2021

5. Algorithm-Based Fault Tolerance for Convolutional Neural Networks

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