Accelerating 3D Cellular automata computation with GP-GPU in the context of integrative biology

Jonathan Caux (1) , David R.C. Hill (2) , Pridi Siregar (3)

1 LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes
2 LIMOS - Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
3 IBC - Integrative BioComputing

In this paper we explore the possibility of using GP GPU technology (General Purpose Graphical Processing Unit) in the context of integrative biology. For more than a decade, 3D cellular automata represent a promising approach to handling multi-scale modeling of organs. However, the computing time of such huge automata has limited the experiments. Current GP GPUs now allow the execution of hundreds of threads with a regular PC hosting a device card. This capability can be exploited in the case of cellular automata where each cell has to compute the same algorithm. We have implemented two algorithms to compare different memory usage. The performances show very significant speedup even when compared to the latest CPU processors. The interconnection of GP GPU boards and servers will be considered to build a local grid of hybrid machines.

Mots clés: GP GPU; Parallel computing; 3D cellular automata; Integrative Biology

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Jonathan Caux, David R.C. Hill, Pridi Siregar. Accelerating 3D Cellular automata computation with GP-GPU in the context of integrative biology. Cellular Automata - Innovative Modelling for Science and Engineering, InTech, pp.411-426, 2011. ⟨hal-00679045⟩