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<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="https://puma.ub.uni-stuttgart.de/tag/programming%20parallel"><owl:Ontology rdf:about=""><rdfs:comment>PUMA publications for /tag/programming%20parallel</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="https://puma.ub.uni-stuttgart.de/bibtex/2ca592479b0e6cdd08604a1b7461cca0a/c.niethammer"><owl:sameAs rdf:resource="/uri/bibtex/2ca592479b0e6cdd08604a1b7461cca0a/c.niethammer"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Misc"/><owl:sameAs rdf:resource="https://eurompi2018.bsc.es/sites/default/files/uploaded/EuroMPI2018_Niethammer%2BRabenseifner_ML-Cartesian_e-abstract.pdf"/><swrc:date>Thu Jan 31 21:11:23 CET 2019</swrc:date><swrc:month>sep</swrc:month><swrc:title>Topology aware Cartesian grid mapping with MPI</swrc:title><swrc:year>2018</swrc:year><swrc:keywords>Cartesian MPI grid hlrs network parallel programming spmt topology </swrc:keywords><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Christoph Niethammer"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Rolf Rabenseifner"/></rdf:_2></rdf:Seq></swrc:author></rdf:Description><rdf:Description rdf:about="https://puma.ub.uni-stuttgart.de/bibtex/200e71607cd89c36f3f5bb856e79e7b39/c.niethammer"><owl:sameAs rdf:resource="/uri/bibtex/200e71607cd89c36f3f5bb856e79e7b39/c.niethammer"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Book"/><owl:sameAs rdf:resource="http://dx.doi.org/10.1007/978-3-319-39589-0"/><swrc:date>Tue Dec 12 10:44:52 CET 2017</swrc:date><swrc:publisher><swrc:Organization swrc:name="Springer"/></swrc:publisher><swrc:title>Tools for High Performance Computing 2015</swrc:title><swrc:year>2016</swrc:year><swrc:keywords>debugging hlrs parallel performance programming spmt toosl </swrc:keywords><swrc:hasExtraField><swrc:Field swrc:value="978-3-319-39589-0" swrc:key="isbn"/></swrc:hasExtraField><swrc:hasExtraField><swrc:Field swrc:value="UB" swrc:key="library"/></swrc:hasExtraField><swrc:hasExtraField><swrc:Field swrc:value="Proceedings of the 9th International Workshop on Parallel Tools for High Performance Computing, September 2015, Dresden, Germany" swrc:key="subtitle"/></swrc:hasExtraField><swrc:hasExtraField><swrc:Field swrc:value="10.1007/978-3-319-39589-0" swrc:key="doi"/></swrc:hasExtraField><swrc:editor><rdf:Seq><rdf:_1><swrc:Person swrc:name="Andreas Knüpfer"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Tobias Hilbrich"/></rdf:_2><rdf:_3><swrc:Person swrc:name="Christoph Niethammer"/></rdf:_3><rdf:_4><swrc:Person swrc:name="José Gracia"/></rdf:_4><rdf:_5><swrc:Person swrc:name="Wolfgang E. 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Proceedings</swrc:booktitle><swrc:pages>1--12</swrc:pages><swrc:publisher><swrc:Organization swrc:name="Springer Berlin Heidelberg"/></swrc:publisher><swrc:title>591 TFLOPS Multi-trillion Particles Simulation on SuperMUC</swrc:title><swrc:year>2013</swrc:year><swrc:keywords>dynamics highly molecular parallel programming scalable simulation simulations vectorization </swrc:keywords><swrc:abstract>Anticipating large-scale molecular dynamics simulations (MD) in nano-fluidics, we conduct performance and scalability studies of an optimized version of the code ls1 mardyn. We present our implementation requiring only 32 Bytes per molecule, which allows us to run the, to our knowledge, largest MD simulation to date. Our optimizations tailored to the Intel Sandy Bridge processor are explained, including vectorization as well as shared-memory parallelization to make use of Hyperthreading. 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