Supercomputers Ready for Use as Discovery Machines for Neuroscience - Helias, Moritz; Kunkel, Susanne; Masumoto, Gen; Igarashi, Jun; Eppler, Jochen M.; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus (Frontiers in Neuroinformatics, 2012)
NEST is a widely used tool to simulate biological spiking neural networks. Here we explain the improvements, guided by a mathematical model of memory consumption, that enable us to exploit for the first time the computational power of the K supercomputer for neuroscience. Multi-threaded components for wiring and simulation combine 8 cores per MPI process to achieve excellent scaling. K is capable of simulating networks corresponding to a brain area with 10^8 neurons and 10^12 synapses in the worst case scenario of random connectivity; for larger networks of the brain its hierarchical organization can be exploited to constrain the number of communicating computer nodes. We discuss the limits of the software technology, comparing maximum-□lling scaling plots for K and the JUGENE BG/P system. The usability of these machines for network simulations has become comparable to running simulations on a single PC. Turn-around times in the range of minutes even for the largest systems enable a quasi-interactive working style and render simulations on this scale a practical tool for computational neuroscience.