Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units

被引：11

作者：

Komarov, Ivan ^{[1
]}

D'Souza, Roshan M. ^{[1
]}

Tapia, Jose-Juan ^{[2
]}

机构：

[1] Univ Wisconsin, Dept Mech Engn, Complex Syst Simulat Lab, Milwaukee, WI 53201 USA

[2] Univ Pittsburgh, Dept Computat Biol, Pittsburgh, PA USA

来源：

PLOS ONE | 2012年 / 7卷 / 06期

基金：

美国国家科学基金会;

关键词：

EXACT STOCHASTIC SIMULATION; SIZE SELECTION; SYSTEMS; ALGORITHM;

D O I：

10.1371/journal.pone.0037370

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The Gillespie tau-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap tau is quite expensive. In this paper, we explore the acceleration of the tau-Leaping Method using Graphics Processing Unit (GPUs) for ultra-large networks (>0.5e(6) reaction channels). We have developed data structures and algorithms that take advantage of the unique hardware architecture and available libraries. Our results show that we obtain a performance gain of over 60x when compared with the best conventional implementations.

引用

页数：7

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