Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers

Abstract

To understand the principles of information processing in the brain, we depend on models with more than 10⁵ neurons and 10⁹ connections. These networks can be described as graphs of threshold elements that exchange point events over their connections.

From the computer science perspective, the key challenges are to represent the connections succinctly; to transmit events and update neuron states efficiently; and to provide a comfortable user interface. We present here the neural simulation tool NEST, a neuronal network simulator which addresses all these requirements. To simulate very large networks with acceptable time and memory requirements, NEST uses a hybrid strategy, combining distributed simulation across cluster nodes (MPI) with thread-based simulation on each computer. Benchmark simulations of a computationally hard biological neuronal network model demonstrate that hybrid parallelization yields significant performance benefits on clusters of multi-core computers, compared to purely MPI-based distributed simulation.

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.