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The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code
2017 (English)In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 11, article id 40Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA , 2017. Vol. 11, article id 40
Keywords [en]
profiling, performance analysis, memory footprint, high-performance computing, supercomputer, large-scale simulation, spiking neuronal networks
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-212349DOI: 10.3389/fninf.2017.00040ISI: 000406560700001PubMedID: 28701946OAI: oai:DiVA.org:kth-212349DiVA, id: diva2:1134789
Note

QC 20170821

Available from: 2017-08-21 Created: 2017-08-21 Last updated: 2017-11-29Bibliographically approved

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