Literature DB >> 18379867

Fully implicit parallel simulation of single neurons.

Michael L Hines1, Henry Markram, Felix Schürmann.   

Abstract

When a multi-compartment neuron is divided into subtrees such that no subtree has more than two connection points to other subtrees, the subtrees can be on different processors and the entire system remains amenable to direct Gaussian elimination with only a modest increase in complexity. Accuracy is the same as with standard Gaussian elimination on a single processor. It is often feasible to divide a 3-D reconstructed neuron model onto a dozen or so processors and experience almost linear speedup. We have also used the method for purposes of load balance in network simulations when some cells are so large that their individual computation time is much longer than the average processor computation time or when there are many more processors than cells. The method is available in the standard distribution of the NEURON simulation program.

Mesh:

Year:  2008        PMID: 18379867      PMCID: PMC2760991          DOI: 10.1007/s10827-008-0087-5

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  11 in total

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Review 8.  The NEURON simulation environment.

Authors:  M L Hines; N T Carnevale
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10.  Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors.

Authors:  Michael L Hines; Hubert Eichner; Felix Schürmann
Journal:  J Comput Neurosci       Date:  2008-01-23       Impact factor: 1.621

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  28 in total

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5.  Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors.

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Journal:  J Comput Neurosci       Date:  2008-01-23       Impact factor: 1.621

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