基于平行粒子的反应扩散:一个GPU实现

Infinity Pub Date : 2010-09-30 DOI:10.1109/PDMC-HIBI.2010.18
Lorenzo Dematté
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引用次数: 10

摘要

空间是生化模型仿真中非常重要的一个方面,近年来,对能够适应空间的仿真算法的需求越来越迫切。复杂和大型的生化系统模型需要处理单个分子和粒子的运动,同时考虑到局部波动、运输现象和扩散。空间模型的一个共同缺点在于它们的复杂性:模型可能变得非常大,并且它们的模拟可能非常耗时,特别是如果我们想要使用随机方法结合高空间分辨率以可靠的方式捕获系统行为。为了实现系统生物学所做的承诺,能够从整体上理解一个系统,我们需要从顺序模拟算法转向并行模拟算法。在本文中,我们分析了Smoldyn,一种广泛传播的具有空间分辨率和单分子细节的化学反应随机模拟算法,并提出了一种利用gpu并行性的替代创新实现。该实现提供了良好的加速(高达130倍)和实时,高质量的图形输出,几乎没有性能损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel Particle-Based Reaction Diffusion: A GPU Implementation
Space is a very important aspect in the simulation of biochemical models, recently, the need for simulation algorithms able to cope with space is becoming more and more compelling. Complex and large models of biochemical systems need to deal with the movement of single molecules and particles, taking into consideration localised fluctuations, transportation phenomena and diffusion. A common drawback of spatial models lies in their complexity: models could become very large, and their simulation could be time consuming, especially if we want to capture the systems behaviour in a reliable way using stochastic methods in conjunction with a high spatial resolution. In order to deliver the promise done by systems biology to be able to understand a system as whole, we need to move from sequential to parallel simulation algorithms. In this paper we analyse Smoldyn, a widely diffused algorithm for stochastic simulation of chemical reactions with spatial resolution and single molecule detail, and we propose an alternative, innovative implementation that exploits the parallelism of GPUs. The implementation offers good speedups (up to 130x) and real time, high quality graphics output at almost no performance penalties.
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来源期刊
CiteScore
2.30
自引率
0.00%
发文量
26
审稿时长
10 weeks
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