ArticleFrontiers in neuroinformatics2017
The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code.
Article in Frontiers in neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The Cerebellar Connectome.Cerebellum (London, England) · 2026Article
- A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.Frontiers in neuroinformatics · 2022Article
- Neural Synchronization, Chimera States and Sleep Asymmetry.Frontiers in network physiology · 2021Article
- Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.Frontiers in neuroinformatics · 2018Article
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2 authors.
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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.
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