Data Optimised Computing for Heterogeneous Big Data Computing Applications

被引：0
|
作者：
Yang, Erica [1 ]
Ross, Derek [1 ]
Nagella, Srikanth [1 ]
Turner, Martin [1 ]
Kockelmann, Winfried [2 ]
Burca, Genoveva [2 ]
Pouzols, Federico Montesino [2 ]
机构：
[1] Sci & Technol Facil Council, Rutherford Appleton Lab, Dept Comp Sci, Harwell Sci & Innovat Campus, Oxford, Oxon, England
[2] Sci & Technol Facil Council, Rutherford Appleton Lab, ISIS Neutron Facil, Oxford, Oxon, England
来源：
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA | 2015年
关键词：
Data Intensive Science; Data Intensive Computing Platform; Facility Science; HPC resource optimisation; Data Analysis Platform;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The rise of big science techniques is reshaping the provisioning of computing resources and scientific software in large science facilities. As facilities are gearing up for data intensive computing infrastructure, a wave of facility-based big science computing platforms is emerging. This paper presents a new computing paradigm towards designing HPC data analysis platform, named Data Optimised Computing (DOC). The DOC paradigm leverages the characteristics of science data to optimize HPC resource utilization and to improve users' ability to harness a variety of scientific analysis software frameworks. We present a preliminary architectural design of a software platform that implements this approach and also discuss the future directions of this work.
引用
收藏
页码：2817 / 2819
页数：3
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