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| Home > Publications database > The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code |
| Home > Publications database > The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code |
| Journal Article | FZJ-2017-04342 |
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2017
Frontiers Research Foundation
Lausanne
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Please use a persistent id in citations: http://hdl.handle.net/2128/14769 doi:10.3389/fninf.2017.00040
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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