Load balancing for multi-threaded PDES of stochastic reaction-diffusion in neurons

Abstract

Chemical reactions and molecular diffusion in a neuron play an important role in the transmission of signals within a neuron. Discrete event stochastic simulation of the chemical reactions and diffusion provides a more detailed view of the molecular dynamics within a neuron than continuous simulation. As part of the NEURON project we developed a multi-threaded optimistic PDES simulator, Neuron Time Warp-Multi Thread, for these reaction-diffusion models. We used NTW-MT to simulate a calcium wave model due to its importance to the neuroscience community and representativeness of the types of reaction-diffusion problems which need to be solved in neuroscience. During the course of our experiments we observed a decided need for load balancing and window control to achieve large-scale runs. In this paper, we improved the Q-Learning and Simulated Annealing load balancing algorithm according to characteristics of reaction and diffusion model to address both of these issues. We evaluated the algorithms by various parameters in various scales, and our results showed that (1) the algorithm improves the execution time for small simulations by up to 31% (using Q-Learning) and 19% (using SA) and (2) the SA approach is more suitable for larger models, decreasing the execution time by 41%.

Keywords:

• stochastic neuronal simulation
• multi-threaded PDES
• load balancing
• window control
• Q-Learning
• simulated annealing

Acknowledgments

This work is supported by China Scholarship Council and in part by the National Natural Science Foundation of China (No. 61170048), Research Project of State Key Laboratory of High Performance Computing of National University of Defense Technology of China (No. 201303-05), and the Research Fund for the Doctoral Program of High Education of China (No. 20124307110017). This work is also funded by U.S. National Institutes of Health grants R01MH086638 and T15LM007056.