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State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.

Non-uniform memory access (NUMA) systems, where each processor has its own memory, have been popular platform in high-end computing. While some early studies had reported that a flat-MPI programming model outperformed an OpenMP/MPI hybrid programming model on SMP clusters, the hybrid of a shared-memory, thread- based programming and a distributed-memory, message passing programming is considered to be a promising programming model on the multi-core multi-socket NUMA clusters. We explore the performance of the OpenMP/MPI hybrid programming model on a large scale multi-core multi-socket cluster called T2K Open Supercomputer to reveal the nature of the hybrid programming model on such clusters. We extend the Real-Space Density Functional Theory (RSDFT) code to the OpenMP/MPI hybrid version. We show and discuss the performance for several routines in RSDFT code which have different characteristics in operation and communication.