• 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.

• Proceedings of the KACD Conference
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• 2008.05a
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• pp.169-176
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• 2008

The purpose of this study was to evaluate the effect of thickness, filling methods and curing methods on the polymerization of dual cured core materials by means of microhardness test. Two dual cured core materials, MultiCore Flow (Ivoclar Vivadent AG, Schaan, Liechtenstein) and Bis-Core (Bisco Inc., Schaumburg, IL, USA) were used in this study. 2 mm (bulky filled), 4 mm (bulky filled), 6 mm (bulky and incrementally filled) and 8 mm (bulky and incrementally filled)-thickness specimens were prepared with light cure or self cure mode. After storage at $37^{\circ}C$ for 24 hours, the Knoop hardness values (KHN) of top and bottom surfaces were measured and the microhardness ratio of top and bottom surfaces was calculated. The data were analyzed using one-way ANOVA and Scheffe multiple comparison test, with ${\alpha}=0.05$. The effect of thickness on the polymerization of dual cured composites showed material specific results. In 2, 4 and 6 mm groups, the KHN of two materials were not affected by thickness. However, in 8 mm group of MultiCore Flow, the KHN of the bottom surface was lower than those of other groups (p < 0.05). The effect of filling methods on the polymerization of dual cured composites was different by their thickness or materials. In 6 mm thickness, there was no significant difference between bulk and incremental filling groups. In 8 mm thickness, Bis-Core showed no significant difference between groups. However, in MultiCore Flow, the microhardness ratio of bulk filling group was lower than that of incremental filling group (p < 0.05). The effect of curing methods on the polymerization of dual cured composites showed material specific results. In Bis-Core, the KHN of dual cured group were higher than those of self cured group at both surfaces (p < 0.05). However, in MultiCore Flow, the results were not similar at both surfaces. At the top surface, dual cured group showed higher KHN than that of self cured group (p < 0.05). However, in the bottom surface, dual cured group showed lower value than that of self cured group (p < 0.05).