Parallel network simulations with NEURON

TitleParallel network simulations with NEURON
Publication TypeJournal Article
Year of Publication2006
AuthorsMigliore, Michele, Cannia C, Lytton William W., Markram Henry, and Hines M. L.
JournalJournal of computational neuroscience
KeywordsComputer simulation, Parallel computation, Realistic modeling, Spiking networks

The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.

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Preprint available as parallel_nets_2006.pdf 
NEURON can be used to implement distributed models of networks that will run in a parallel computation environment (multiprocessor PCs, workstation clusters, or parallel supercomputer architectures). Speedup is proportional to the number of processors until each processor is handling only about 100 equations. Properly written code will run without change on single processor, standalone PCs.