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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. The simulator was developed as part of the NEURON project and is intended to be included in NEURON. It relies upon a stochastic discrete event model developed for chemical reactions. NTW-MT is optimistic and thread-based, in which communication latency among threads within the same process is minimized by pointers. We investigate the performance of NTW-MT on a reaction-diffusion model for the transmission of calcium waves in a neuron. Calcium plays a fundamental role in the second messenger system of a neuron. However, the mechanism by which calcium waves are transmitted is not entirely understood. Stochastic models are more realistic than deterministic models for small populations of ions such as those found in apical dendrites. To be more precise, we simulate a stochastic discrete event model for calcium wave propagation on an unbranched apical dendrite of a hippocampal pyramidal neuron. We examine the performance of NTW-MT on this calcium wave model and compare it to the performance of (1) a process based op- * This author is affiliated with State timistic simulator and (2) a threaded simulator which uses a single priority (SQ) queue for each thread. Our multithreaded simulator is shown to achieve superior performance to these simulators.
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.
Stochastic simulation of chemical reactions and diffusion in a neuron helps to provide a realistic view of the molecular dynamics within a neuron. We developed a multi-threaded PDES simulator, Neuron Time Warp-Multi Thread, suitable for the stochastic simulation of reaction and diffusion in a neuron. In this paper we make use of Q-Learning and Simulated Annealing to determine the parameters for a dynamic load balancing algorithm and for dynamic window control. During the simulation, the runtime statistics of each thread are collected and used to determine the execution time of the simulation. Based upon this assessment, workload is migrated from the most overloaded threads to the most under-load ones. As the results for a calcium wave model show, both approaches can improve the execution time for small simulations by up to 31% (Q-Learning) and 19% (SA). The simulated annealing approach is more suitable for larger populations, decreasing execution time by 41%.
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