• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.6-8
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• 2022

병렬처리시스템이 설계와 구축에서 가장 중요한 관점 중의 하나는 비용 대비 성능이다. 본 연구에서는 라즈베리파이 4를 클러스터 방식으로 연결하여 병렬처리 시스템을 구축하였을 때, 클러스터의 병렬처리 성능이 다른 병렬처리 시스템과 유사한 확장성과 병렬처리 성능을 보여주는지를 HPL 벤치마크를 통하여 검증하였다. 실험 결과 라즈베리파이 기반의 병렬처리 시스템이 클러스터의 크기에 따른 병렬 확장성이 있고, 다른 병렬처리 시스템들과 유사한 처리 성능을 가질 수 있음을 확인하였으며, 이를 통하여 라즈베리파이와 같은 저가의 처리장치로도 충분한 크기의 클러스터를 구성할 경우 높은 성능을 기대할 수 있음을 알 수 있다.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.