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000906899 1001_ $$0P:(DE-Juel1)180539$$aAlbers, Jasper$$b0$$eCorresponding author
000906899 245__ $$aA Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
000906899 260__ $$aLausanne$$bFrontiers Research Foundation$$c2022
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000906899 500__ $$a21 pages, 8 figures, 1 listing
000906899 520__ $$aModern 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.
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000906899 536__ $$0G:(GEPRIS)368482240$$aGRK 2416:  MultiSenses-MultiScales: Novel approaches to decipher neural processing in multisensory integration (368482240)$$c368482240$$x5
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000906899 7001_ $$0P:(DE-Juel1)165321$$aPronold, Jari$$b1
000906899 7001_ $$0P:(DE-Juel1)176776$$aKurth, Anno$$b2
000906899 7001_ $$0P:(DE-HGF)0$$aVennemo, Stine Brekke$$b3
000906899 7001_ $$0P:(DE-HGF)0$$aMood, Kaveh Haghighi$$b4
000906899 7001_ $$0P:(DE-Juel1)179111$$aPatronis, Alexander$$b5
000906899 7001_ $$0P:(DE-Juel1)169778$$aTerhorst, Dennis$$b6
000906899 7001_ $$0P:(DE-HGF)0$$aJordan, Jakob$$b7
000906899 7001_ $$0P:(DE-HGF)0$$aKunkel, Susanne$$b8
000906899 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b9
000906899 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b10
000906899 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b11
000906899 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2022.837549$$gVol. 16, p. 837549$$p837549$$tFrontiers in neuroinformatics$$v16$$x1662-5196$$y2022
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