High-Performance Computing with TeraStat

被引:5
作者
Bompiani, Edoardo [1 ]
Petrillo, Umberto Ferraro [1 ]
Lasinio, Giovanna Jona [1 ]
Palini, Francesco [1 ]
机构
[1] Univ Roma La Sapienza, Dipartimento Sci Stat, I-00185 Rome, Italy
来源
2020 IEEE INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, INTL CONF ON CLOUD AND BIG DATA COMPUTING, INTL CONF ON CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH) | 2020年
关键词
Distributed Computing; High-Performance Computing; Bioinformatics; FORMULATION;
D O I
10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00092
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the last years we are witnessing a progressive shift toward the adoption of the cloud-computing model for a large variety of application domains. High-performance computing (HPC) is not an exception to this trend, with a steadily increasing amount of performance-heavy scientific experiments that are conducted on the cloud. However, this approach may pose some serious security, operational and economical issues. In this paper we report about our experience with this scenario, that culminated with the development of a small-scale on-premise HPC facility called TeraStat, available through our Department. This facility has been purposely deployed and customized so to simplify and accelerate the execution of performance-heavy computational experiments. The success of TeraStat is confirmed by the large number of experiments conducted in these years. Here, we focus on two of the most significant case studies, outlining the positive role played by TeraStat in their solution. The first is a large scale analysis of climate data with annual cycles. The second is a study about the advantages of using compression techniques while processing large genomic data by means of a distributed approach.
引用
收藏
页码:499 / 506
页数:8
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