Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure
Metadatos
Mostrar el registro completo del ítemEditorial
Frontiers
Materia
Spiking neural networks Embodiment Neurorobotics Platform High performance computing (HPC) NEST Musculoskeletal modeling Large-scale brain simulation Parallel computing
Fecha
2022-05-19Referencia bibliográfica
Feldotto B... [et al.] (2022) Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure. Front. Neuroinform. 16:884180. doi: [10.3389/fninf.2022.884180]
Patrocinador
European Union’s Horizon 2020 Framework Programme 785907 945539; European Union’s Horizon 2020 800858; MEXT (hp200139, hp210169) MEXT KAKENHI grant no. 17H06310.Resumen
Simulating the brain-body-environment trinity in closed loop is an attractive proposal
to investigate how perception, motor activity and interactions with the environment
shape brain activity, and vice versa. The relevance of this embodied approach, however,
hinges entirely on the modeled complexity of the various simulated phenomena. In this
article, we introduce a software framework that is capable of simulating large-scale,
biologically realistic networks of spiking neurons embodied in a biomechanically accurate
musculoskeletal system that interacts with a physically realistic virtual environment. We
deploy this framework on the high performance computing resources of the EBRAINS
research infrastructure and we investigate the scaling performance by distributing
computation across an increasing number of interconnected compute nodes. Our
architecture is based on requested compute nodes as well as persistent virtualmachines;
this provides a high-performance simulation environment that is accessible to multidomain
users without expert knowledge, with a view to enable users to instantiate
and control simulations at custom scale via a web-based graphical user interface. Our
simulation environment, entirely open source, is based on the Neurorobotics Platform
developed in the context of the Human Brain Project, and the NEST simulator. We
characterize the capabilities of our parallelized architecture for large-scale embodied
brain simulations through two benchmark experiments, by investigating the effects of
scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a largescale
balanced network, while the second one is a multi-region embodied brain
simulation consisting of more than a million neurons and a billion synapses. Both
benchmarks clearly show how scaling compute resources improves the aforementioned
performance metrics in a near-linear fashion. The second benchmark in particular is
indicative of both the potential and limitations of a highly distributed simulation in
terms of a trade-off between computation speed and resource cost. Our simulation
architecture is being prepared to be accessible for everyone as an EBRAINS service,
thereby offering a community-wide tool with a unique workflow that should provide
momentum to the investigation of closed-loop embodiment within the computational
neuroscience community.