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Data optimised computing for heterogeneous big data computing applications

Authors: Erica Yang; Derek Ross; Srikanth Nagella; Martin J. Turner; Winfried Kockelmann; Genoveva Burca; Federico Montesino-Pouzols;

Data optimised computing for heterogeneous big data computing applications

Abstract

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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