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Title: An evaluation environment for high-performance computing combining supercomputing and cloud

Authors: Yusuke Gotoh; Toshihiro Kotani

Addresses: Faculty of Natural Science and Technology, Okayama University, Okayama 7008530, Japan ' Graduate School of Natural Science and Technology, Okayama University, Okayama 7008530, Japan

Abstract: In recent computer environments that use a wide variety of data, users need a system that can efficiently process data according to the scale and type of data. There are two types of typical computing environments: supercomputers and cloud systems. Cloud services can provide computer resources based on the scale of the computer environment desired by users. On the other hand, in conventional large-scale computer environment that only consists of CPUs or GPUs, the processing time greatly increases according to the scale of the calculation processing. Based on the characteristics of the supercomputer and the cloud system installed in the Hokkaido University Information Initiative Center, Japan, we aim to construct a high-performance computing environment by linking the two types of systems. In this paper, we propose an evaluation environment for high-performance computing combining supercomputing and cloud systems. Our proposed system can construct a high-performance computing environment based on the scale of the computing process by the cooperation of the supercomputing and cloud systems with short physical distance and short network distance. In our evaluation of deep reinforcement learning using our proposed system, we confirmed that computer resources can be effectively used by allocating suitable processing for the supercomputer and the cloud according to the usage situations of the CPU, the GPU, and the memory.

Keywords: cloud service; high-performance computing; processing time; supercomputer.

DOI: 10.1504/IJGUC.2023.129701

International Journal of Grid and Utility Computing, 2023 Vol.14 No.1, pp.29 - 36

Received: 17 Oct 2020
Accepted: 22 Feb 2021

Published online: 21 Mar 2023 *

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