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| 001 | 905830 | ||
| 005 | 20240313103118.0 | ||
| 024 | 7 | _ | |a arXiv:2112.09018 |2 arXiv |
| 024 | 7 | _ | |a 2128/30532 |2 Handle |
| 024 | 7 | _ | |a altmetric:119255611 |2 altmetric |
| 037 | _ | _ | |a FZJ-2022-01050 |
| 100 | 1 | _ | |a Albers, Jasper |0 P:(DE-Juel1)180539 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1643102478_11645 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 500 | _ | _ | |a 32 pages, 8 figures, 1 listing |
| 520 | _ | _ | |a Modern 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. |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
| 536 | _ | _ | |a DEEP-EST - DEEP - Extreme Scale Technologies (754304) |0 G:(EU-Grant)754304 |c 754304 |f H2020-FETHPC-2016 |x 2 |
| 536 | _ | _ | |a ACA - Advanced Computing Architectures (SO-092) |0 G:(DE-HGF)SO-092 |c SO-092 |x 3 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 4 |
| 536 | _ | _ | |a GRK 2416: MultiSenses-MultiScales: Novel approaches to decipher neural processing in multisensory integration (368482240) |0 G:(GEPRIS)368482240 |c 368482240 |x 5 |
| 536 | _ | _ | |a MetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026) |0 G:(DE-Juel-1)ZT-I-PF-3-026 |c ZT-I-PF-3-026 |x 6 |
| 588 | _ | _ | |a Dataset connected to arXivarXiv |
| 700 | 1 | _ | |a Pronold, Jari |0 P:(DE-Juel1)165321 |b 1 |u fzj |
| 700 | 1 | _ | |a Kurth, Anno Christopher |0 P:(DE-Juel1)176776 |b 2 |u fzj |
| 700 | 1 | _ | |a Vennemo, Stine Brekke |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Mood, Kaveh Haghighi |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Patronis, Alexander |0 P:(DE-Juel1)179111 |b 5 |
| 700 | 1 | _ | |a Terhorst, Dennis |0 P:(DE-Juel1)169778 |b 6 |u fzj |
| 700 | 1 | _ | |a Jordan, Jakob |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Kunkel, Susanne |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 9 |u fzj |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 10 |u fzj |
| 700 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 11 |u fzj |
| 773 | _ | _ | |y 2021 |t arXiv |
| 856 | 4 | _ | |u https://arxiv.org/abs/2112.09018 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/905830/files/2112.09018.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:905830 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5234 |x 0 |
| 914 | 1 | _ | |y 2021 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l no |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
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