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@INPROCEEDINGS{Albers:908388,
      author       = {Albers, Jasper and Pronold, Jari and Kurth, Anno and
                      Vennemo, Stine Brekke and Haghighi Mood, Kaveh and Patronis,
                      Alexander and Terhorst, Dennis and Jordan, Jakob and Kunkel,
                      Susanne and Tetzlaff, Tom and Diesmann, Markus and Senk,
                      Johanna},
      title        = {be{NN}ch – {F}inding {P}erformance {B}ottlenecks of
                      {N}euronal {N}etwork {S}imulators},
      reportid     = {FZJ-2022-02583},
      year         = {2022},
      abstract     = {Modern computational neuroscience seeks to explain the
                      dynamics and function of the brain by constructing models
                      with ever more biological detail. This can, for example,
                      take the form of sophisticated connectivity schemes [1] or
                      involve the simultaneous simulation of multiple brain areas
                      [2]. To enable progress in these studies, the simulation of
                      models needs to become faster, calling for more efficient
                      implementations of the underlying simulators. Performance
                      benchmark- ing guides software development since it is hard
                      to predict the impact of algorithm adaptations on the
                      performance of complex software such as neuronal network
                      simulators [3]. The particular challenge for these
                      simulators is that executing benchmarks naturally involves
                      the simulation of a diverse range of network models as they
                      may uncover different performance limitations due to their
                      variation in size, synaptic density and distribution of
                      delays [4]. In addition, maintain- ing an accessible library
                      of past results while keeping track of metadata that
                      specifies hardware, software, simulator and model
                      configurations is a difficult task. Here, we introduce
                      beNNch [5] – a recently developed framework for
                      benchmarking neuronal network simulations – and walk
                      through a typical use case, highlighting how it simplifies
                      workflows and enables sustainable use of computing
                      resources.[1] Billeh, Y. N., Cai, B., Gratiy, S. L., Dai,
                      K., Iyer, R., Gouwens, N. W., et al. (2020). System- atic
                      Integration of Structural and Functional Data into
                      Multi-scale Models of Mouse Primary Visual Cortex. Neuron
                      106, 388-403.e18. doi: 10.1016/j.neuron.2020.01.040 [2]
                      Schmidt, M., Bakker, R., Hilgetag, C. C., Diesmann, M., and
                      van Albada, S. J. (2018a). Multi-scale ac- count of the
                      network structure of macaque visual cortex. Brain Struct
                      Funct. 223, 1409–1435. doi: 10.1007/s00429-017-1554-4 [3]
                      Jordan, J., Ippen, T., Helias, M., Kitayama, I., Sato, M.,
                      Igarashi, J., et al. (2018). Extremely scalable spiking
                      neuronal network simulation code: from laptops to exascale
                      computers. Front. Neuroinform. 12:2. doi:
                      10.3389/fninf.2018.00002 [4] Albers, J., Pronold, J., Kurth,
                      A. C., Vennemo, S. B., Haghighi Mood, K., Patronis, A., et
                      al. (in press). A Modular Workflow for Performance
                      Benchmarking of Neuronal Network Simulations. Front.
                      Neuroinform. doi: 10.3389/fninf.2022.837549 [5]
                      https://github.com/INM-6/beNNch},
      month         = {Jun},
      date          = {2022-06-23},
      organization  = {NEST Conference, virtual (Germany), 23
                       Jun 2022 - 24 Jun 2022},
      subtyp        = {After Call},
      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) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      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 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/908388},
}