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@ARTICLE{Kunkel:834370,
      author       = {Kunkel, Susanne and Schenck, Wolfram},
      title        = {{T}he {NEST} {D}ry-{R}un {M}ode: {E}fficient {D}ynamic
                      {A}nalysis of {N}euronal {N}etwork {S}imulation {C}ode},
      journal      = {Frontiers in neuroinformatics},
      volume       = {11},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2017-04342},
      pages        = {40},
      year         = {2017},
      abstract     = {NEST is a simulator for spiking neuronal networks that
                      commits to a general purpose approach: It allows for high
                      flexibility in the design of network models, and its
                      applications range from small-scale simulations on laptops
                      to brain-scale simulations on supercomputers. Hence,
                      developers need to test their code for various use cases and
                      ensure that changes to code do not impair scalability.
                      However, running a full set of benchmarks on a supercomputer
                      takes up precious compute-time resources and can entail long
                      queuing times. Here, we present the NEST dry-run mode, which
                      enables comprehensive dynamic code analysis without
                      requiring access to high-performance computing facilities. A
                      dry-run simulation is carried out by a single process, which
                      performs all simulation steps except communication as if it
                      was part of a parallel environment with many processes. We
                      show that measurements of memory usage and runtime of
                      neuronal network simulations closely match the corresponding
                      dry-run data. Furthermore, we demonstrate the successful
                      application of the dry-run mode in the areas of profiling
                      and performance modeling.},
      cin          = {JSC / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / Brain-Scale Simulations
                      $(jinb33_20121101)$ / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      $G:(DE-Juel1)jinb33_20121101$ / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000406560700001},
      pubmed       = {pmid:28701946},
      doi          = {10.3389/fninf.2017.00040},
      url          = {https://juser.fz-juelich.de/record/834370},
}