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000841394 037__ $$aFZJ-2017-08469
000841394 041__ $$aEnglish
000841394 1001_ $$0P:(DE-Juel1)162390$$aGötz, Markus$$b0$$eCorresponding author$$ufzj
000841394 1112_ $$aWorkshop on Software Engineering for High Performance Computing in Computational and Data-enabled Science & Engineering$$cDenver$$d2017-11-12 - 2017-11-17$$gSE-CoDeSe 17$$wUSA
000841394 245__ $$aSupporting Software Engineering Practices in the Development of Data-Intensive HPC Applications with the JuML Framework
000841394 260__ $$bACM Press$$c2017
000841394 29510 $$aProceedings of the 1st International Workshop on Software Engineering for High Performance Computing in Computational and Data-enabled Science & Engineering
000841394 300__ $$a1-8
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000841394 520__ $$aThe development of high performance computing applications is considerably different from traditional software development. 	This distinction is due to the complex hardware systems, inherent parallelism, different software lifecycle and workflow, as well as (especially for scientific computing applications) partially unknown requirements at design time. 	This makes the use of software engineering practices challenging, so only a small subset of them are actually applied. 	In this paper, we discuss the potential for applying software engineering techniques to an emerging field in high performance computing, namely large-scale data analysis and machine learning. 	We argue for the employment of software engineering techniques in the development of such applications from the start, and the design of generic, reusable components. 	Using the example of the Juelich Machine Learning Library (JuML), we demonstrate how such a framework can not only simplify the design of new parallel algorithms, but also increase the productivity of the actual data analysis workflow. 	We place particular focus on the abstraction from heterogeneous hardware, the architectural design as well as aspects of parallel and distributed unit testing.
000841394 536__ $$0G:(DE-HGF)POF3-512$$a512 - Data-Intensive Science and Federated Computing (POF3-512)$$cPOF3-512$$fPOF III$$x0
000841394 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x1
000841394 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x2
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000841394 7001_ $$0P:(DE-HGF)0$$aBook, Matthias$$b1
000841394 7001_ $$0P:(DE-Juel1)164357$$aBodenstein, Christian$$b2$$ufzj
000841394 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b3$$ufzj
000841394 773__ $$a10.1145/3144763.3144765
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