Journal Article

FZJ-2022-01754

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations

Albers, J. (Corresponding author)FZJ* ; Pronold, J.FZJ* ; Kurth, A.FZJ* ; Vennemo, S. B.Extern* ; Mood, K. H.FZJ* ; Patronis, A.FZJ* ; Terhorst, D.FZJ* ; Jordan, J.Extern* ; Kunkel, S.Extern* ; Tetzlaff, T.FZJ* ; Diesmann, M.FZJ* ; Senk, J.FZJ*

2022
Frontiers Research Foundation Lausanne

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Please use a persistent id in citations: http://hdl.handle.net/2128/31404  doi:10.3389/fninf.2022.837549

Abstract: Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availability of detailed anatomical data on brain connectivity. Large-scale models that study interactions between multiple brain areas with intricate connectivity and investigate phenomena on long time scales such as system-level learning require progress in simulation speed. The corresponding development of state-of-the-art simulation engines relies on information provided by benchmark simulations which assess the time-to-solution for scientifically relevant, complementary network models using various combinations of hardware and software revisions. However, maintaining comparability of benchmark results is difficult due to a lack of standardized specifications for measuring the scaling performance of simulators on high-performance computing (HPC) systems. Motivated by the challenging complexity of benchmarking, we define a generic workflow that decomposes the endeavor into unique segments consisting of separate modules. As a reference implementation for the conceptual workflow, we develop beNNch: an open-source software framework for the configuration, execution, and analysis of benchmarks for neuronal network simulations. The framework records benchmarking data and metadata in a unified way to foster reproducibility. For illustration, we measure the performance of various versions of the NEST simulator across network models with different levels of complexity on a contemporary HPC system, demonstrating how performance bottlenecks can be identified, ultimately guiding the development toward more efficient simulation technology.

Note: 21 pages, 8 figures, 1 listing

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Contributing Institute(s):

1. Jara-Institut Brain structure-function relationships (INM-10)
2. Theoretical Neuroscience (IAS-6)
3. Computational and Systems Neuroscience (INM-6)

Research Program(s):

1. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)
2. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
3. DEEP-EST - DEEP - Extreme Scale Technologies (754304) (754304)
4. ACA - Advanced Computing Architectures (SO-092) (SO-092)
5. JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) (JL SMHB-2021-2027)
6. GRK 2416:  MultiSenses-MultiScales: Novel approaches to decipher neural processing in multisensory integration (368482240) (368482240)
7. Open-Access-Publikationskosten Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)
8. MetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026) (ZT-I-PF-3-026)
9. PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) (PHD-NO-GRANT-20170405)

Appears in the scientific report 2022

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