000908613 001__ 908613
000908613 005__ 20240313095015.0
000908613 037__ $$aFZJ-2022-02722
000908613 041__ $$aEnglish
000908613 1001_ $$0P:(DE-Juel1)191583$$aVillamar, Jose$$b0$$eCorresponding author$$ufzj
000908613 1112_ $$aNEST Conference 2022$$cOnline$$d2022-06-23 - 2022-06-24$$wGermany
000908613 245__ $$aNEST is on the road to GPU integration
000908613 260__ $$c2022
000908613 3367_ $$033$$2EndNote$$aConference Paper
000908613 3367_ $$2BibTeX$$aINPROCEEDINGS
000908613 3367_ $$2DRIVER$$aconferenceObject
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000908613 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1661321305_27668$$xAfter Call
000908613 520__ $$aMost of the Top500 computer systems and all of the upcoming exascale machines employ GPUs alongside CPUs. To get the most performance out of these architectures, simulation software requires efficient support for both processor types. Decades of simulator development enable the routine simulation of large-scale neuronal network models on thousands of many-core CPUs in parallel [1]; recent GPU implementations show highly competitive results [2, 3]. Here, we present our project to integrate NEST GPU (formerly NeuronGPU [3]) into the ecosystem of the CPU-based simulator NEST [4]. NEST GPU, written in CUDA-C++, lends itself to this integration due to a similar interface and a modular structure. The development will continue within the NEST Initiative under the same GitHub organization [5], although the codes themselves are still separate. We pursue the unified, community-centered workflow already pioneered by NEST: build processes, model development (NESTML [6]), documentation standards along with quality assurance through continuous integration. We are looking forward to a fruitful exchange between NEST and NEST GPU, enabling the optimization of simulator performance under the hood while providing a common frontend for users to seamlessly harness both CPUs and GPUs in the future.
000908613 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000908613 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x1
000908613 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x2
000908613 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x3
000908613 536__ $$0G:(DE-Juel-1)ZT-I-PF-3-026$$aMetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026)$$cZT-I-PF-3-026$$x4
000908613 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x5
000908613 536__ $$0G:(DE-Juel1)BMBF-03ZU1106CB$$aBMBF-03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - B (BMBF-03ZU1106CB)$$cBMBF-03ZU1106CB$$x6
000908613 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x7
000908613 536__ $$0G:(DE-Juel1)jinb33_20191101$$aBrain-Scale Simulations (jinb33_20191101)$$cjinb33_20191101$$fBrain-Scale Simulations$$x8
000908613 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x9
000908613 7001_ $$0P:(DE-HGF)0$$aGianmarco, Tiddia$$b1
000908613 7001_ $$0P:(DE-Juel1)176305$$aLinssen, Charl$$b2$$ufzj
000908613 7001_ $$0P:(DE-Juel1)186954$$aBabu, Pooja$$b3$$ufzj
000908613 7001_ $$0P:(DE-HGF)0$$aPastorelli, Elena$$b4
000908613 7001_ $$0P:(DE-HGF)0$$aPaolucci, Pier Stanislao$$b5
000908613 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b6$$ufzj
000908613 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b7$$ufzj
000908613 7001_ $$0P:(DE-HGF)0$$aGolosio, Bruno$$b8
000908613 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b9$$ufzj
000908613 909CO $$ooai:juser.fz-juelich.de:908613$$pec_fundedresources$$pVDB$$popenaire
000908613 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191583$$aForschungszentrum Jülich$$b0$$kFZJ
000908613 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176305$$aForschungszentrum Jülich$$b2$$kFZJ
000908613 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186954$$aForschungszentrum Jülich$$b3$$kFZJ
000908613 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich$$b6$$kFZJ
000908613 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich$$b7$$kFZJ
000908613 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162130$$aForschungszentrum Jülich$$b9$$kFZJ
000908613 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000908613 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x1
000908613 9141_ $$y2022
000908613 920__ $$lno
000908613 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000908613 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000908613 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000908613 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x3
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000908613 980__ $$aI:(DE-Juel1)INM-10-20170113
000908613 980__ $$aI:(DE-Juel1)IAS-6-20130828
000908613 980__ $$aI:(DE-Juel1)JSC-20090406
000908613 980__ $$aUNRESTRICTED
000908613 981__ $$aI:(DE-Juel1)IAS-6-20130828