School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom.
J Chem Phys. 2012 Jan 7;136(1):014101. doi: 10.1063/1.3670416.
In recent years, computer simulations have become increasingly useful when trying to understand the complex dynamics of biochemical networks, particularly in stochastic systems. In such situations stochastic simulation is vital in gaining an understanding of the inherent stochasticity present, as these models are rarely analytically tractable. However, a stochastic approach can be computationally prohibitive for many models. A number of approximations have been proposed that aim to speed up stochastic simulations. However, the majority of these approaches are fundamentally serial in terms of central processing unit (CPU) usage. In this paper, we propose a novel simulation algorithm that utilises the potential of multi-core machines. This algorithm partitions the model into smaller sub-models. These sub-models are then simulated, in parallel, on separate CPUs. We demonstrate that this method is accurate and can speed-up the simulation by a factor proportional to the number of processors available.
近年来，计算机模拟在试图理解生化网络的复杂动态方面变得越来越有用，特别是在随机系统中。在这种情况下，随机模拟对于理解存在的固有随机性至关重要，因为这些模型很少是可分析的。然而，对于许多模型来说，随机方法可能在计算上是不可行的。已经提出了许多旨在加快随机模拟的近似方法。然而，这些方法中的大多数在中央处理器 (CPU) 使用方面本质上是串行的。在本文中，我们提出了一种新颖的模拟算法，利用多核机器的潜力。该算法将模型划分为较小的子模型。然后在单独的 CPU 上并行模拟这些子模型。我们证明了这种方法是准确的，可以将模拟速度提高到与可用处理器数量成正比的倍数。
