%0 Journal Article
%A Kunkel, Susanne
%A Schenck, Wolfram
%T The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code
%J Frontiers in neuroinformatics
%V 11
%@ 1662-5196
%C Lausanne
%I Frontiers Research Foundation
%M FZJ-2017-04342
%P 40
%D 2017
%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.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000406560700001
%$ pmid:28701946
%R 10.3389/fninf.2017.00040
%U https://juser.fz-juelich.de/record/834370