Data optimised computing for heterogeneous big data computing applications

Autor:	Srikanth Nagella, Martin Turner, Genoveva Burca, Federico Montesino Pouzols, Winfried Kockelmann, Derek Ross, Erica Yang
Rok vydání:	2015
Předmět:	Computer science business.industry Big data Provisioning computer.software_genre Data science Variety (cybernetics) Software framework Software Utility computing End-user computing Data-intensive computing business computer
Zdroj:	IEEE BigData
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::52e556bc3a05ab82f3c3c09867090a35 https://doi.org/10.1109/bigdata.2015.7364087 Zobrazit plný text záznamu