title: Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors.
abstract: Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Splitting cells is useful in attaining load balance in neural network simulations, especially when there is a wide range of cell sizes and the number of cells is about the same as the number of processors. For compute-bound simulations load balance results in almost ideal runtime scaling. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing.
authors.affiliation: Computer Science, Yale University, New Haven, CT, USA. michael.hines@yale.edu
authors.forename: Michael L
authors.initials: ML
authors.lastname: Hines
authors.forename: Hubert
authors.initials: H
authors.lastname: Eichner
authors.forename: Felix
authors.initials: F
authors.lastname: Schürmann
mesh_terms: D002540:Cerebral Cortex / Q000502:physiology; D003198:Computer Simulation; D018891:Dentate Gyrus / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D016011:Normal Distribution; D013787:Thalamic Nuclei / Q000502:physiology