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000905830 005__ 20240313103118.0
000905830 0247_ $$2arXiv$$aarXiv:2112.09018
000905830 0247_ $$2Handle$$a2128/30532
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000905830 037__ $$aFZJ-2022-01050
000905830 1001_ $$0P:(DE-Juel1)180539$$aAlbers, Jasper$$b0$$eCorresponding author$$ufzj
000905830 245__ $$aA Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
000905830 260__ $$c2021
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000905830 500__ $$a32 pages, 8 figures, 1 listing
000905830 520__ $$aModern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availability of detailed anatomical data on brain connectivity. Large-scale models that study interactions between multiple brain areas with intricate connectivity and investigate phenomena on long time scales such as system-level learning require progress in simulation speed. The corresponding development of state-of-the-art simulation engines relies on information provided by benchmark simulations which assess the time-to-solution for scientifically relevant, complementary network models using various combinations of hardware and software revisions. However, maintaining comparability of benchmark results is difficult due to a lack of standardized specifications for measuring the scaling performance of simulators on high-performance computing (HPC) systems. Motivated by the challenging complexity of benchmarking, we define a generic workflow that decomposes the endeavor into unique segments consisting of separate modules. As a reference implementation for the conceptual workflow, we develop beNNch: an open-source software framework for the configuration, execution, and analysis of benchmarks for neuronal network simulations. The framework records benchmarking data and metadata in a unified way to foster reproducibility. For illustration, we measure the performance of various versions of the NEST simulator across network models with different levels of complexity on a contemporary HPC system, demonstrating how performance bottlenecks can be identified, ultimately guiding the development toward more efficient simulation technology.
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000905830 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000905830 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x2
000905830 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x3
000905830 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x4
000905830 536__ $$0G:(GEPRIS)368482240$$aGRK 2416:  MultiSenses-MultiScales: Novel approaches to decipher neural processing in multisensory integration (368482240)$$c368482240$$x5
000905830 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)$$cZT-I-PF-3-026$$x6
000905830 588__ $$aDataset connected to arXivarXiv
000905830 7001_ $$0P:(DE-Juel1)165321$$aPronold, Jari$$b1$$ufzj
000905830 7001_ $$0P:(DE-Juel1)176776$$aKurth, Anno Christopher$$b2$$ufzj
000905830 7001_ $$0P:(DE-HGF)0$$aVennemo, Stine Brekke$$b3
000905830 7001_ $$0P:(DE-HGF)0$$aMood, Kaveh Haghighi$$b4
000905830 7001_ $$0P:(DE-Juel1)179111$$aPatronis, Alexander$$b5
000905830 7001_ $$0P:(DE-Juel1)169778$$aTerhorst, Dennis$$b6$$ufzj
000905830 7001_ $$0P:(DE-HGF)0$$aJordan, Jakob$$b7
000905830 7001_ $$0P:(DE-HGF)0$$aKunkel, Susanne$$b8
000905830 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b9$$ufzj
000905830 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b10$$ufzj
000905830 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b11$$ufzj
000905830 773__ $$tarXiv$$y2021
000905830 8564_ $$uhttps://arxiv.org/abs/2112.09018
000905830 8564_ $$uhttps://juser.fz-juelich.de/record/905830/files/2112.09018.pdf$$yOpenAccess
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000905830 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000905830 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000905830 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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