000862062 001__ 862062
000862062 005__ 20210130001146.0
000862062 0247_ $$2doi$$a10.1177/1094342018778123
000862062 0247_ $$2ISSN$$a1078-3482
000862062 0247_ $$2ISSN$$a1094-3420
000862062 0247_ $$2ISSN$$a1741-2846
000862062 0247_ $$2WOS$$aWOS:000438959200001
000862062 0247_ $$2altmetric$$aaltmetric:45121063
000862062 037__ $$aFZJ-2019-02426
000862062 082__ $$a004
000862062 1001_ $$0P:(DE-HGF)0$$aAsch, M.$$b0
000862062 245__ $$aBig data and extreme-scale computing: Pathways to Convergence-Toward ashaping strategy for a future software and data ecosystem for scientific inquiry
000862062 260__ $$aThousand Oaks, Calif.$$bSage Science Press$$c2018
000862062 3367_ $$2DRIVER$$aarticle
000862062 3367_ $$2DataCite$$aOutput Types/Journal article
000862062 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1554386223_19287
000862062 3367_ $$2BibTeX$$aARTICLE
000862062 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000862062 3367_ $$00$$2EndNote$$aJournal Article
000862062 520__ $$aOver the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth acrossthe computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an accountof some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.
000862062 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000862062 588__ $$aDataset connected to CrossRef
000862062 7001_ $$0P:(DE-HGF)0$$aMoore, T.$$b1
000862062 7001_ $$0P:(DE-HGF)0$$aBadia, R.$$b2
000862062 7001_ $$0P:(DE-HGF)0$$aBeck, M.$$b3
000862062 7001_ $$0P:(DE-HGF)0$$aBeckman, P.$$b4
000862062 7001_ $$0P:(DE-HGF)0$$aBidot, T.$$b5
000862062 7001_ $$0P:(DE-HGF)0$$aBodin, F.$$b6
000862062 7001_ $$0P:(DE-HGF)0$$aCappello, F.$$b7
000862062 7001_ $$0P:(DE-HGF)0$$aChoudhary, A.$$b8
000862062 7001_ $$0P:(DE-HGF)0$$ade Supinski, B.$$b9
000862062 7001_ $$0P:(DE-HGF)0$$aDeelman, E.$$b10
000862062 7001_ $$0P:(DE-HGF)0$$aDongarra, J.$$b11$$eCorresponding author
000862062 7001_ $$0P:(DE-HGF)0$$aDubey, A.$$b12
000862062 7001_ $$0P:(DE-HGF)0$$aFox, G.$$b13
000862062 7001_ $$0P:(DE-HGF)0$$aFu, H.$$b14
000862062 7001_ $$0P:(DE-HGF)0$$aGirona, S.$$b15
000862062 7001_ $$0P:(DE-HGF)0$$aGropp, W.$$b16
000862062 7001_ $$0P:(DE-HGF)0$$aHeroux, M.$$b17
000862062 7001_ $$0P:(DE-HGF)0$$aIshikawa, Y.$$b18
000862062 7001_ $$0P:(DE-HGF)0$$aKeahey, K.$$b19
000862062 7001_ $$0P:(DE-HGF)0$$aKeyes, D.$$b20
000862062 7001_ $$0P:(DE-HGF)0$$aKramer, W.$$b21
000862062 7001_ $$0P:(DE-HGF)0$$aLavignon, J-F$$b22
000862062 7001_ $$0P:(DE-HGF)0$$aLu, Y.$$b23
000862062 7001_ $$0P:(DE-HGF)0$$aMatsuoka, S.$$b24
000862062 7001_ $$0P:(DE-Juel1)132199$$aMohr, B.$$b25$$ufzj
000862062 7001_ $$0P:(DE-HGF)0$$aReed, D.$$b26
000862062 7001_ $$0P:(DE-HGF)0$$aRequena, S.$$b27
000862062 7001_ $$0P:(DE-HGF)0$$aSaltz, J.$$b28
000862062 7001_ $$0P:(DE-HGF)0$$aSchulthess, T.$$b29
000862062 7001_ $$0P:(DE-HGF)0$$aStevens, R.$$b30
000862062 7001_ $$0P:(DE-HGF)0$$aSwany, M.$$b31
000862062 7001_ $$0P:(DE-HGF)0$$aSzalay, A.$$b32
000862062 7001_ $$0P:(DE-HGF)0$$aTang, W.$$b33
000862062 7001_ $$0P:(DE-HGF)0$$aVaroquaux, G.$$b34
000862062 7001_ $$0P:(DE-HGF)0$$aVilotte, J-P$$b35
000862062 7001_ $$0P:(DE-HGF)0$$aWisniewski, R.$$b36
000862062 7001_ $$0P:(DE-HGF)0$$aXu, Z.$$b37
000862062 7001_ $$0P:(DE-HGF)0$$aZacharov, I.$$b38
000862062 773__ $$0PERI:(DE-600)2017480-9$$a10.1177/1094342018778123$$gVol. 32, no. 4, p. 435 - 479$$n4$$p435 - 479$$tThe international journal of high performance computing applications$$v32$$x1741-2846$$y2018
000862062 8564_ $$uhttps://juser.fz-juelich.de/record/862062/files/1094342018778123.pdf$$yRestricted
000862062 8564_ $$uhttps://juser.fz-juelich.de/record/862062/files/1094342018778123.pdf?subformat=pdfa$$xpdfa$$yRestricted
000862062 909CO $$ooai:juser.fz-juelich.de:862062$$pVDB
000862062 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132199$$aForschungszentrum Jülich$$b25$$kFZJ
000862062 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000862062 9141_ $$y2019
000862062 915__ $$0StatID:(DE-HGF)0410$$2StatID$$aAllianz-Lizenz
000862062 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000862062 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000862062 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bINT J HIGH PERFORM C : 2017
000862062 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000862062 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000862062 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000862062 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000862062 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000862062 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000862062 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000862062 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000862062 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000862062 920__ $$lno
000862062 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000862062 980__ $$ajournal
000862062 980__ $$aVDB
000862062 980__ $$aI:(DE-Juel1)JSC-20090406
000862062 980__ $$aUNRESTRICTED