• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2001.08a
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• pp.125-130
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• 2001

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.13-19
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• 2019

The accuracy of machine learning is greatly affected by amount of learning data and quality of data. Collecting existing Web-based learning data has danger that data unrelated to actual learning can be collected, and it is impossible to secure data transparency. In this paper, we propose a method for collecting data directly in parallel by blocks in a block - chain structure, and comparing the data collected by each block with data in other blocks to select only good data. In the proposed system, each block shares data with each other through a chain of blocks, utilizes the All-reduce structure of Parallel-SGD to select only good quality data through comparison with other block data to construct a learning data set. Also, in order to verify the performance of the proposed architecture, we verify that the original image is only good data among the modulated images using the existing benchmark data set.

• Journal of Soil and Groundwater Environment
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• v.28 no.2
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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.