title: HRLSim: a high performance spiking neural network simulator for GPGPU clusters.
abstract: Modeling of large-scale spiking neural models is an important tool in the quest to understand brain function and subsequently create real-world applications. This paper describes a spiking neural network simulator environment called HRL Spiking Simulator (HRLSim). This simulator is suitable for implementation on a cluster of general purpose graphical processing units (GPGPUs). Novel aspects of HRLSim are described and an analysis of its performance is provided for various configurations of the cluster. With the advent of inexpensive GPGPU cards and compute power, HRLSim offers an affordable and scalable tool for design, real-time simulation, and analysis of large-scale spiking neural networks.
authors.forename: Kirill
authors.initials: K
authors.lastname: Minkovich
authors.forename: Corey M
authors.initials: CM
authors.lastname: Thibeault
authors.forename: Michael John
authors.initials: MJ
authors.lastname: O'Brien
authors.forename: Aleksey
authors.initials: A
authors.lastname: Nogin
authors.forename: Youngkwan
authors.initials: Y
authors.lastname: Cho
authors.forename: Narayan
authors.initials: N
authors.lastname: Srinivasa
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology