Dynamic Reconfiguration of Computer Platforms at the Hardware Device Level for High Performance Computing Infrastructure as a Service
Dynamic Reconfiguration of Computer Platforms at the Hardware Device Level for High Performance Computing Infrastructure as a Service
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DOI：
10.1007/978-3-319-94959-8_10
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发表时间：
2017-04
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影响因子：
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通讯作者：
Akihiro Misawa;S. Date;Keichi Takahashi;Takashi Yoshikawa;Masahiko Takahashi;Masaki Kan;Yasuhiro Watashiba;Y. Kido;Chonho Lee;S. Shimojo
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文献类型：
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作者：
Akihiro Misawa;S. Date;Keichi Takahashi;Takashi Yoshikawa;Masahiko Takahashi;Masaki Kan;Yasuhiro Watashiba;Y. Kido;Chonho Lee;S. Shimojo
用户对高性能计算（HPC）的需求和要求已经变得越来越多样化。随着用户需求的日益多样化，自身拥有高性能计算平台变得越来越困难，并且HPC平台提供商需要提供计算平台以执行多样化的应用。在本文中，我们提出了一个计算机架构，提供HPC基础设施动态和及时的云计算服务，以响应用户对计算平台的要求。为了获得灵活性，以适应各种HPC作业与应用程序特定的计算平台，所提出的系统重新配置的软件和硬件平台，利用协同作用的开放网格计算/网格引擎和OpenStack。本研究开发的实验系统显示了硬件平台重构的高度灵活性和Spark基准测试应用的高性能。此外，我们的模拟评估表明，动态可重构硬件集群系统可以提高硬件资源利用率，并消除了我校计算机中心2016年上半年实际运行记录中最严重的资源拥塞情况。
Users’ needs and requirements for high performance computing (HPC) has become increasingly diversified. As user needs become increasingly diverse, it becomes increasingly difficult to own high-performance computing platforms themselves and the HPC platform provider are required to provide computing platforms to execute diverse applications. In this paper, we propose a computer architecture for providing HPC infrastructure dynamically and promptly as a cloud computing service in response to users’ request for computing platforms. In order to gain flexibility to accommodate various HPC jobs with application specific computing platforms, the proposed system reconfigures a software and hardware platform by utilizing the synergy of Open Grid Scheduler/Grid Engine and OpenStack. The experimental system developed in this research shows the high flexibility of hardware platform reconfiguration and the high performance of Spark’s benchmark application. In addition, our simulation evaluation shows that dynamic reconfigurable hardware cluster system can improve hardware resource utility rate, and also eliminating the worst case of resource congestion in the real-world operational record of our university’s computer center during the first half of 2016.