Semi-dynamic load balancing

Author:

Chen Chen1,Weng Qizhen1,Wang Wei1,Li Baochun2,Li Bo1

Affiliation:

1. HKUST

2. University of Toronto

Funder

RGC GRF

Publisher

ACM

Reference89 articles.

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2. 2019. Train Deep Learning Models on GPUs using Amazon EC2 Spot Instances. https://aws.amazon.com/en/blogs/machine-learning/train-deep-learning-models-on-gpus-using-amazon-ec2-spot-instances/. 2019. Train Deep Learning Models on GPUs using Amazon EC2 Spot Instances. https://aws.amazon.com/en/blogs/machine-learning/train-deep-learning-models-on-gpus-using-amazon-ec2-spot-instances/.

3. 2020. Apache Thrift. https://thrift.apache.org/. 2020. Apache Thrift. https://thrift.apache.org/.

4. 2020. EC2 Spot Instances. https://aws.amazon.com/ec2/spot/. 2020. EC2 Spot Instances. https://aws.amazon.com/ec2/spot/.

5. 2020. FloydHub. https://floydhub.com/. 2020. FloydHub. https://floydhub.com/.

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2. AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

3. Heet: Accelerating Elastic Training in Heterogeneous Deep Learning Clusters;Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2;2024-04-27

4. Locality-Aware and Fault-Tolerant Batching for Machine Learning on Distributed Datasets;IEEE Transactions on Cloud Computing;2024-04

5. LBB: load-balanced batching for efficient distributed learning on heterogeneous GPU cluster;The Journal of Supercomputing;2024-02-09

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