The K computer Operations: Experiences and Statistics

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure

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 virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain 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 large-scale 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.

A model of the indirect losses from negative shocks in production and finance

Economies are frequently affected by natural disasters and both domestic and overseas financial crises. These events disrupt production and cause multiple other types of economic losses, including negative impacts on the banking system. Understanding the transmission mechanism that causes various negative second-order post-catastrophe effects is crucial if policymakers are to develop more efficient recovery strategies. In this work, we introduce a credit-based adaptive regional input-output (ARIO) model to analyse the effects of disasters and crises on the supply chain and bank-firm credit networks. Using real Japanese networks and the exogenous shocks of the 2008 Lehman Brothers bankruptcy and the Great East Japan Earthquake (March 11, 2011), this paper aims to depict how these negative shocks propagate through the supply chain and affect the banking system. The credit-based ARIO model is calibrated using Latin hypercube sampling and the design of experiments procedure to reproduce the short-term (one-year) dynamics of the Japanese industrial production index after the 2008 Lehman Brothers bankruptcy and the 2011 Great East Japan earthquake. Then, through simulation experiments, we identify the chemical and petroleum manufacturing and transport sectors as the most vulnerable Japanese industrial sectors. Finally, the case of the 2011 Great East Japan Earthquake is simulated for Japanese prefectures to understand differences among regions in terms of globally engendered indirect economic losses. Tokyo and Osaka prefectures are the most vulnerable locations because they hold greater concentrations of the above-mentioned vulnerable industrial sectors.