AUTHOR=Kunkel Susanne , Schenck Wolfram TITLE=The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code JOURNAL=Frontiers in Neuroinformatics VOLUME=11 YEAR=2017 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2017.00040 DOI=10.3389/fninf.2017.00040 ISSN=1662-5196 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.