The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code
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 tes...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-06-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fninf.2017.00040/full |
| _version_ | 1811195121256890368 |
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| author | Susanne Kunkel Susanne Kunkel Wolfram Schenck Wolfram Schenck |
| author_facet | Susanne Kunkel Susanne Kunkel Wolfram Schenck Wolfram Schenck |
| author_sort | Susanne Kunkel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T00:38:15Z |
| format | Article |
| id | doaj.art-1624609497a54c2fbba71764e4d7a64f |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-04-12T00:38:15Z |
| publishDate | 2017-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-1624609497a54c2fbba71764e4d7a64f2022-12-22T03:55:06ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962017-06-011110.3389/fninf.2017.00040249600The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation CodeSusanne Kunkel0Susanne Kunkel1Wolfram Schenck2Wolfram Schenck3Simulation Laboratory Neuroscience, Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Forschungszentrum JülichJülich, GermanyDepartment of Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of TechnologyStockholm, SwedenSimulation Laboratory Neuroscience, Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Forschungszentrum JülichJülich, GermanyFaculty of Engineering and Mathematics, Bielefeld University of Applied SciencesBielefeld, GermanyNEST 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.http://journal.frontiersin.org/article/10.3389/fninf.2017.00040/fullprofilingperformance analysismemory footprinthigh-performance computingsupercomputerlarge-scale simulation |
| spellingShingle | Susanne Kunkel Susanne Kunkel Wolfram Schenck Wolfram Schenck The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code Frontiers in Neuroinformatics profiling performance analysis memory footprint high-performance computing supercomputer large-scale simulation |
| title | The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code |
| title_full | The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code |
| title_fullStr | The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code |
| title_full_unstemmed | The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code |
| title_short | The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code |
| title_sort | nest dry run mode efficient dynamic analysis of neuronal network simulation code |
| topic | profiling performance analysis memory footprint high-performance computing supercomputer large-scale simulation |
| url | http://journal.frontiersin.org/article/10.3389/fninf.2017.00040/full |
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