Coarse-grain parallel computing for very large scale neural simulations in the NEXUS simulation environment

Abstract

We describe a neural simulator designed for simulating very large scale models of cortical architectures. This simulator, NEXUS, uses coarse-grain parallel computing by distributing computation and data onto multiple conventional workstations connected via a local area network. Coarse-grain parallel computing offers natural advantages in simulating functionally segregated neural processes. We partition a complete model into modules with locally dense connections—a module may represent a cortical area, column, layer, or functional entily. Asynchronous data communications among workstations are established through the Network File System, which, together with the implicit modularity, decreases communications overhead, and increases overall performance. Coarse-grain parallelism also benefits from the standardization of conventional workstations and LAN, including portability between generations and vendors.