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000834093 005__ 20210129230537.0
000834093 020__ $$a978-3-319-58942-8 (print)
000834093 020__ $$a978-3-319-58943-5 (electronic)
000834093 0247_ $$2doi$$a10.1007/978-3-319-58943-5_39
000834093 0247_ $$2ISSN$$a0302-9743
000834093 0247_ $$2ISSN$$a1611-3349
000834093 0247_ $$2WOS$$aWOS:000529303100039
000834093 037__ $$aFZJ-2017-04094
000834093 082__ $$a004
000834093 1001_ $$0P:(DE-HGF)0$$aGrunzke, Richard$$b0$$eCorresponding author
000834093 1112_ $$aEuropean Conference on Parallel Processing$$cGrenoble$$d2016-08-24 - 2016-08-26$$gEuro-Par$$wFrance
000834093 245__ $$aSeamless HPC Integration of Data-Intensive KNIME Workflows via UNICORE
000834093 260__ $$aCham$$bSpringer International Publishing$$c2017
000834093 29510 $$aEuro-Par 2016: Parallel Processing Workshops / Desprez, Frederic (Editor)   ; Cham : Springer International Publishing, 2017, Chapter 39 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-319-58942-8=978-3-319-58943-5 ; doi:10.1007/978-3-319-58943-5
000834093 300__ $$a480 - 491
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000834093 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000834093 4900_ $$aLecture Notes in Computer Science$$v10104
000834093 520__ $$aBiological research is increasingly dependent on analyzing vast amounts of microscopy datasets. Technologies such as Fiji/ImageJ2 and KNIME support knowledge extraction from biological data by providing a large set of configurable algorithms and an intuitive pipeline creation and execution interface. The increasing complexity of required analysis pipelines and the growing amounts of data to be processed nurture the desire to run existing pipelines on HPC (High Performance Computing) systems. Here, we propose a solution to this challenge by presenting a new HPC integration method for KNIME (Konstanz Information Miner) using the UNICORE middleware (Uniform Interface to Computing Resources) and its automated data processing feature. We designed the integration to be efficient in processing large data workloads on the server side. On the client side it is seamless and lightweight to only minimally increase the complexity for the users. We describe our novel approach and evaluate it using an image processing pipeline that could previously not be executed on an HPC system. The evaluation includes a performance study of the induced overhead of the submission process and of the integrated image processing pipeline based on a large amount of data. This demonstrates how our solution enables scientists to transparently benefit from vast HPC resources without the need to migrate existing algorithms and pipelines.
000834093 536__ $$0G:(DE-HGF)POF3-512$$a512 - Data-Intensive Science and Federated Computing (POF3-512)$$cPOF3-512$$fPOF III$$x0
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000834093 7001_ $$0P:(DE-HGF)0$$aJug, Florian$$b1$$eCorresponding author
000834093 7001_ $$0P:(DE-Juel1)132261$$aSchuller, Bernd$$b2
000834093 7001_ $$0P:(DE-HGF)0$$aJäkel, Rene$$b3
000834093 7001_ $$0P:(DE-HGF)0$$aMyers, Gene$$b4
000834093 7001_ $$0P:(DE-HGF)0$$aNagel, Wolfgang E.$$b5
000834093 773__ $$a10.1007/978-3-319-58943-5_39
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000834093 9141_ $$y2017
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