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@ARTICLE{Albers:905830,
      author       = {Albers, Jasper and Pronold, Jari and Kurth, Anno
                      Christopher and Vennemo, Stine Brekke and Mood, Kaveh
                      Haghighi and Patronis, Alexander and Terhorst, Dennis and
                      Jordan, Jakob and Kunkel, Susanne and Tetzlaff, Tom and
                      Diesmann, Markus and Senk, Johanna},
      title        = {{A} {M}odular {W}orkflow for {P}erformance {B}enchmarking
                      of {N}euronal {N}etwork {S}imulations},
      journal      = {arXiv},
      reportid     = {FZJ-2022-01050},
      year         = {2021},
      note         = {32 pages, 8 figures, 1 listing},
      abstract     = {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.},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      DEEP-EST - DEEP - Extreme Scale Technologies (754304) / ACA
                      - Advanced Computing Architectures (SO-092) / JL SMHB -
                      Joint Lab Supercomputing and Modeling for the Human Brain
                      (JL SMHB-2021-2027) / GRK 2416:  MultiSenses-MultiScales:
                      Novel approaches to decipher neural processing in
                      multisensory integration (368482240) / MetaMoSim - Generic
                      metadata management for reproducible
                      high-performance-computing simulation workflows - MetaMoSim
                      (ZT-I-PF-3-026)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(EU-Grant)945539 /
                      G:(EU-Grant)754304 / G:(DE-HGF)SO-092 / G:(DE-Juel1)JL
                      SMHB-2021-2027 / G:(GEPRIS)368482240 /
                      G:(DE-Juel-1)ZT-I-PF-3-026},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2112.09018},
      howpublished = {arXiv:2112.09018},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2112.09018;\%\%$},
      url          = {https://juser.fz-juelich.de/record/905830},
}