• 한국해안해양공학회:학술대회논문집
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• 한국해안해양공학회 2001년도 한국해안해양공학발표논문집 Proceedings of Coastal and Ocean Engineering in Korea
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• pp.125-130
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• 2001

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.911-931
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• 2021

Recently, most cloud services use Docker container environment to provide their services. However, there are no researches to evaluate the performance of communication libraries for multi-GPU based distributed deep learning in a Docker container environment. In this paper, we propose an efficient communication architecture for multi-GPU based deep learning in a Docker container environment by evaluating the performances of various communication libraries. We compare the performances of the parameter server architecture and the All-reduce architecture, which are typical distributed deep learning architectures. Further, we analyze the performances of two separate multi-GPU resource allocation policies - allocating a single GPU to each Docker container and allocating multiple GPUs to each Docker container. We also experiment with the scalability of collective communication by increasing the number of GPUs from one to four. Through experiments, we compare OpenMPI and MPICH, which are representative open source MPI libraries, and NCCL, which is NVIDIA's collective communication library for the multi-GPU setting. In the parameter server architecture, we show that using CUDA-aware OpenMPI with multi-GPU per Docker container environment reduces communication latency by up to 75%. Also, we show that using NCCL in All-reduce architecture reduces communication latency by up to 93% compared to other libraries.

• 한국정보통신학회논문지
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• 제23권1호
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• pp.13-19
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• 2019

기계학습의 정확도는 학습용 데이터의 양과 데이터의 품질에 많은 영향을 받는다. 기존의 웹을 기반으로 학습용 데이터를 수집하는 것은 실제 학습과 무관한 데이터가 수집 될 수 있는 위험성이 있으며 데이터의 투명성을 보장할 수가 없다. 본 논문에서는 블록체인구조에서 블록들이 직접 병렬적으로 데이터를 수집하게 하고 각 블록들이 수집한 데이터를 타 블록의 데이터와 비교하여 양질의 데이터만을 선별하는 방안을 제안한다. 제안하는 시스템은 각 블록들은 데이터를 서로 블록체인을 통해 공유하며 All-reduce 구조의 Parallel-SGD를 활용하여 다른 블록들의 데이터와 비교를 통해 양질의 데이터만을 선별하여 학습용 데이터셋을 구성할 수가 있다. 또한 본 논문에서는 제안한 구조의 성능을 확인하기 위해 실험을 통해 기존의 벤치마크용 데이터셋의 이미지를 활용하여 변조된 이미지 사이에서 원본 이미지만을 양질의 데이터로 판별함을 확인하였다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제28권2호
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• pp.1-11
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• 2023

Groundwater-surface water interaction was evaluated using water quality parameters (temperature and electrical conductivity), distributions of stable water isotopes (δ²H and δ ¹⁸O), and Rn-222 in lagoon water, groundwater, and seawater at three coastal lagoons (Songji (SJ), Youngrang (YR), and Sunpo (SP) Lagoon) in South Korea. From the results of composition and distributions of δ²H and δ¹⁸O, it was found that groundwater fraction of lagoon water in YR Lagoon (76%) was slightly higher than those of SJ (42%), and SP (63%) Lagoon. Based on Rn-222 mass balance model, groundwater discharge into SJ Lagoon in summer 2020 was estimated to be (3.2±1.1)×10³ m³ day^-1, which showed a similar or an order of magnitude higher than the results of previous studies conducted in coastal lagoons. This study can provide advanced techniques to evaluate groundwater-surface water interaction in coastal lagoons, wetlands, and lakes, and help to determine the effects of groundwater on coastal ecosystems.