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%.

神经元中的化学反应和分子扩散在神经元内的信号传递中起重要作用。与连续模拟相比，化学反应和扩散的离散事件随机模拟提供了神经元内分子动力学的更详细视图。作为NEURON项目的一部分，我们为这些反应扩散模型开发了多线程乐观PDES模拟器Neuron Time Warp-Multi Thread。由于其对神经科学界的重要性以及神经科学中需要解决的反应扩散问题类型的代表性，因此我们使用NTW-MT来模拟钙波模型。在我们的实验过程中，我们发现确实需要进行负载平衡和窗口控制，以实现大规模运行。在本文中，我们根据反应和扩散模型的特征改进了Q学习和模拟退火负载平衡算法，以解决这两个问题。我们通过各种规模的各种参数对算法进行了评估，结果表明：（1）该算法将小型仿真的执行时间提高了31％（使用Q-Learning）和19％（使用SA），并且（2） SA方法更适合大型模型，将执行时间减少了41％。