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Multithreaded Stochastic PDES for Reactions and Diffusions in Neurons.

Authors:
Zhongwei Lin Carl Tropper Robert A Mcdougal Mohammand Nazrul Ishlam Patoary William W Lytton Yiping Yao Michael L Hines

ACM Trans Model Comput Simul 2017 Jul;27(2)

Department of Neurobiology, Yale University, New Haven, Connecticut, USA;

Cells exhibit stochastic behavior when the number of molecules is small. Hence a stochastic reaction-diffusion simulator capable of working at scale can provide a more accurate view of molecular dynamics within the cell. This paper describes a parallel discrete event simulator, Neuron Time Warp-Multi Thread (NTW-MT), developed for the simulation of reaction diffusion models of neurons. To the best of our knowledge, this is the first parallel discrete event simulator oriented towards stochastic simulation of chemical reactions in a neuron. The simulator was developed as part of the NEURON project. NTW-MT is optimistic and thread-based, which attempts to capitalize on multi-core architectures used in high performance machines. It makes use of a multi-level queue for the pending event set and a single roll-back message in place of individual anti-messages to disperse contention and decrease the overhead of processing rollbacks. Global Virtual Time is computed asynchronously both within and among processes to get rid of the overhead for synchronizing threads. Memory usage is managed in order to avoid locking and unlocking when allocating and de-allocating memory and to maximize cache locality. We verified our simulator on a calcium buffer model. We examined its performance on a calcium wave model, comparing it to the performance of a process based optimistic simulator and a threaded simulator which uses a single priority queue for each thread. Our multi-threaded simulator is shown to achieve superior performance to these simulators. Finally, we demonstrated the scalability of our simulator on a larger CICR model and a more detailed CICR model.

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http://dx.doi.org/10.1145/2987373DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604336PMC
July 2017

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