%A Kunkel,Susanne %A Schenck,Wolfram %D 2017 %J Frontiers in Neuroinformatics %C %F %G English %K profiling,performance analysis,memory footprint,high-performance computing,supercomputer,large-scale simulation,spiking neuronal networks %Q %R 10.3389/fninf.2017.00040 %W %L %M %P %7 %8 2017-June-28 %9 Technology Report %+ Susanne Kunkel,Simulation Laboratory Neuroscience, Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Forschungszentrum Jülich,Jülich, Germany,su.kunkel@fz-juelich.de %+ Susanne Kunkel,Department of Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology,Stockholm, Sweden,su.kunkel@fz-juelich.de %+ Dr Wolfram Schenck,Simulation Laboratory Neuroscience, Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Forschungszentrum Jülich,Jülich, Germany,wolfram.schenck@hsbi.de %+ Dr Wolfram Schenck,Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences,Bielefeld, Germany,wolfram.schenck@hsbi.de %# %! The NEST dry-run mode %* %< %T The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code %U https://www.frontiersin.org/articles/10.3389/fninf.2017.00040 %V 11 %0 JOURNAL ARTICLE %@ 1662-5196 %X 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.