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Title: Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations

Abstract

Spatial stochastic molecular simulations in biology are limited by the intense computation required to track molecules in space either in a discrete time or discrete space framework, which has led to the development of parallel methods that can take advantage of the power of modern supercomputers in recent years. We systematically test suggested components of stochastic reaction-diffusion operator splitting in the literature and discuss their effects on accuracy. We introduce an operator splitting implementation for irregular meshes that enhances accuracy with minimal performance cost. We test a range of models in small-scale MPI simulations from simple diffusion models to realistic biological models and find that multi-dimensional geometry partitioning is an important consideration for optimum performance. We demonstrate performance gains of 1-3 orders of magnitude in the parallel implementation, with peak performance strongly dependent on model specification.

Authors:
;  [1];  [2];  [1]
  1. Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904 0495 (Japan)
  2. (Belgium)
Publication Date:
OSTI Identifier:
22678968
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Chemical Physics; Journal Volume: 145; Journal Issue: 5; Other Information: (c) 2016 Author(s); Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; CALCULATION METHODS; PARTICLE TRACKS; SIMULATION

Citation Formats

Hepburn, I., De Schutter, E., E-mail: erik@oist.jp, Theoretical Neurobiology & Neuroengineering, University of Antwerp, Antwerp 2610, and Chen, W.. Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations. United States: N. p., 2016. Web. doi:10.1063/1.4960034.
Hepburn, I., De Schutter, E., E-mail: erik@oist.jp, Theoretical Neurobiology & Neuroengineering, University of Antwerp, Antwerp 2610, & Chen, W.. Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations. United States. doi:10.1063/1.4960034.
Hepburn, I., De Schutter, E., E-mail: erik@oist.jp, Theoretical Neurobiology & Neuroengineering, University of Antwerp, Antwerp 2610, and Chen, W.. Sun . "Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations". United States. doi:10.1063/1.4960034.
@article{osti_22678968,
title = {Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations},
author = {Hepburn, I. and De Schutter, E., E-mail: erik@oist.jp and Theoretical Neurobiology & Neuroengineering, University of Antwerp, Antwerp 2610 and Chen, W.},
abstractNote = {Spatial stochastic molecular simulations in biology are limited by the intense computation required to track molecules in space either in a discrete time or discrete space framework, which has led to the development of parallel methods that can take advantage of the power of modern supercomputers in recent years. We systematically test suggested components of stochastic reaction-diffusion operator splitting in the literature and discuss their effects on accuracy. We introduce an operator splitting implementation for irregular meshes that enhances accuracy with minimal performance cost. We test a range of models in small-scale MPI simulations from simple diffusion models to realistic biological models and find that multi-dimensional geometry partitioning is an important consideration for optimum performance. We demonstrate performance gains of 1-3 orders of magnitude in the parallel implementation, with peak performance strongly dependent on model specification.},
doi = {10.1063/1.4960034},
journal = {Journal of Chemical Physics},
number = 5,
volume = 145,
place = {United States},
year = {Sun Aug 07 00:00:00 EDT 2016},
month = {Sun Aug 07 00:00:00 EDT 2016}
}