배민호

5.

여상호,; 배민호,; 정민중,; 권오경,; 오상윤,

Crossover-SGD: A gossip-based communication in distributed deep learning for alleviating large mini-batch problem and enhancing scalability Journal Article

In: Concurrency and Computation: Practice and Experience, 2022.

Abstract | Links | BibTeX

@article{여상호2022Crossover-SGD,

title = {Crossover-SGD: A gossip-based communication in distributed deep learning for alleviating large mini-batch problem and enhancing scalability},

author = {여상호 and 배민호 and 정민중 and 권오경 and 오상윤},

url = {https://arxiv.org/abs/2012.15198},

doi = {10.48550/arXiv.2012.15198},

year  = {2022},

date = {2022-11-01},

urldate = {2022-11-01},

journal = {Concurrency and Computation: Practice and Experience},

abstract = {     Distributed deep learning is an effective way to reduce the training time of deep learning for large datasets as well as complex models. However, the limited scalability caused by network overheads makes it difficult to synchronize the parameters of all workers. To resolve this problem, gossip-based methods that demonstrates stable scalability regardless of the number of workers have been proposed. However, to use gossip-based methods in general cases, the validation accuracy for a large mini-batch needs to be verified. To verify this, we first empirically study the characteristics of gossip methods in a large mini-batch problem and observe that the gossip methods preserve higher validation accuracy than AllReduce-SGD(Stochastic Gradient Descent) when the number of batch sizes is increased and the number of workers is fixed. However, the delayed parameter propagation of the gossip-based models decreases validation accuracy in large node scales. To cope with this problem, we propose Crossover-SGD that alleviates the delay propagation of weight parameters via segment-wise communication and load balancing random network topology. We also adapt hierarchical communication to limit the number of workers in gossip-based communication methods. To validate the effectiveness of our proposed method, we conduct empirical experiments and observe that our Crossover-SGD shows higher node scalability than SGP(Stochastic Gradient Push). },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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4.

민경준,; 배민호,; 박규동,; 오상윤,

혼합형 출판-구독 메시징 시스템 구조 제안 및 연동 기법 Journal Article

In: 한국통신학회논문지, vol. 45, no. 4, pp. 748–755, 2020.

Links | BibTeX