Tangherloni Andrea, Nobile Marco S, Besozzi Daniela, Mauri Giancarlo, Cazzaniga Paolo
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy.
SYSBIO.IT Centre of Systems Biology, Piazza della Scienza 2, Milano, 20126, Italy.
BMC Bioinformatics. 2017 May 10;18(1):246. doi: 10.1186/s12859-017-1666-0.
Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs.
LASSIE is a "black-box" GPU-accelerated deterministic simulator, specifically designed for large-scale models and not requiring any expertise in mathematical modeling, simulation algorithms or GPU programming. Given a reaction-based model of a cellular process, LASSIE automatically generates the corresponding system of Ordinary Differential Equations (ODEs), assuming mass-action kinetics. The numerical solution of the ODEs is obtained by automatically switching between the Runge-Kutta-Fehlberg method in the absence of stiffness, and the Backward Differentiation Formulae of first order in presence of stiffness. The computational performance of LASSIE are assessed using a set of randomly generated synthetic reaction-based models of increasing size, ranging from 64 to 8192 reactions and species, and compared to a CPU-implementation of the LSODA numerical integration algorithm.
LASSIE adopts a novel fine-grained parallelization strategy to distribute on the GPU cores all the calculations required to solve the system of ODEs. By virtue of this implementation, LASSIE achieves up to 92× speed-up with respect to LSODA, therefore reducing the running time from approximately 1 month down to 8 h to simulate models consisting in, for instance, four thousands of reactions and species. Notably, thanks to its smaller memory footprint, LASSIE is able to perform fast simulations of even larger models, whereby the tested CPU-implementation of LSODA failed to reach termination. LASSIE is therefore expected to make an important breakthrough in Systems Biology applications, for the execution of faster and in-depth computational analyses of large-scale models of complex biological systems.
数学建模和计算机模拟分析被广泛认为是生物实验室方法的补充工具，用于全面理解细胞过程在生理和受干扰条件下的涌现行为。然而，由数百或数千个反应和分子物种组成的大规模模型的模拟，可能会迅速超出中央处理器（CPU）的能力。这项工作的目的是利用替代的高性能计算解决方案，如图形处理器（GPU），以降低计算成本来研究这些模型。
LASSIE是一个“黑箱”式GPU加速确定性模拟器，专门为大规模模型设计，不需要数学建模、模拟算法或GPU编程方面的任何专业知识。给定一个基于反应的细胞过程模型，LASSIE在假设质量作用动力学的情况下，自动生成相应的常微分方程（ODE）系统。通过在无刚性时自动切换到龙格 - 库塔 - 费尔贝格方法，以及在有刚性时自动切换到一阶向后微分公式，来获得ODE的数值解。使用一组随机生成的、大小不断增加的基于反应的合成模型（反应和物种数量从64到8192不等）来评估LASSIE的计算性能，并与LSODA数值积分算法的CPU实现进行比较。
LASSIE采用了一种新颖的细粒度并行化策略，将求解ODE系统所需的所有计算分布在GPU核心上。凭借这种实现方式，LASSIE相对于LSODA实现了高达92倍的加速，因此将运行时间从大约1个月减少到8小时，以模拟例如包含四千个反应和物种的模型。值得注意的是，由于其较小的内存占用，LASSIE能够对甚至更大的模型进行快速模拟，而测试的LSODA的CPU实现未能达到终止。因此，预计LASSIE将在系统生物学应用中取得重要突破，用于对复杂生物系统的大规模模型进行更快、更深入的计算分析。
