Cross-comparison of state of the art neuromorphological simulators on modern CPUs and GPUs using the Brain Scaffold Builder

• Published in: bioRxiv
• Main Authors: De Schepper, Robin Gl, Nora Abi Akar, Hater, Thorsten, Huisman, Brent Fg, D'angelo, Egidio Ugo, Morrison, Abigail, Casellato, Claudia
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 04.03.2022
• Subjects: Computational neuroscience; Computer programs; Neural networks; Simulation
• DOI: 10.1101/2022.03.02.482285

A variety of software simulators exist for neuronal networks, and a subset of these tools allow the scientist to model neurons in high morphological detail. The scalability of such simulation tools over a wide range in neuronal networks sizes and cell complexities is predominantly limited by effective allocation of components of such simulations over computational nodes, and the overhead in communication between them. In order to have more scalable simulation software, it is therefore important to develop a robust benchmarking strategy that allows insight into specific computational bottlenecks for models of realistic size and complexity. In this study, we demonstrate the use of the Brain Scaffold Builder as a framework for performing such benchmarks. We perform a comparison between the well-known neuromorphological simulator NEURON, and Arbor, a new simulation library developed within the framework of the Human Brain Project. The BSB can construct identical neuromorphological and network setups of highly spatially and biophysically detailed networks for each simulator. This ensures good coverage of feature support in each simulator, and realistic workloads. After validating the outputs of the BSB generated models, we execute the simulations on a variety of hardware configurations consisting of two types of nodes (GPU and CPU). We investigate performance of two different network models, one suited for a single machine, and one for distributed simulation. We investigate performance across different mechanisms, mechanism classes, mechanism combinations, and cell types. Our benchmarks show that, depending on the distribution scheme deployed by Arbor, a speed-up with respect to NEURON of between 60 and 400 can be achieved. Additionally Arbor can be up to two orders of magnitude more energy efficient. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/Helveg/arb-nrn-comp