Towards simulating data-driven brain models at the point neuron level on petascale computers

Abstract

We present a solution to two important problems that arise in the simulation of large data-driven neural networks: (a) efficient loading of network descriptions and (b) efficient instantiation of the network by executing the model specification. To address the first problem, we present a general data-format PointBrainH5, to store the network information along with the parallel-distributed RTC algorithm to efficiently load and instantiate a network model. We test data-format and algorithm on a data-driven simulation of the size of a full mouse brain on 4 racks of a IBM Blue Gene/Q. The model comprised 75 million neurons with 664 billion synapses and occupied 15 TB on disk. Loading and instantiation of the network on 4 racks of the BlueGene/Q took 30min. We observe good scaling for up to 16,384 nodes.