Proceedings of the 9th International Conference on Computational Methods in Systems Biology
High Performance Hybrid Functional Petri Net Simulations of Biological Pathway Models on CUDA
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Smoldyn on Graphics Processing Units: Massively Parallel Brownian Dynamics Simulations
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Optimizing linpack benchmark on GPU-accelerated petascale supercomputer
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
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To address the problem of performing long time simulations of biochemical pathways under in vivo cellular conditions, we have developed a lattice-based, reaction-diffusion model that uses the graphics processing unit (GPU) as a computational co-processor. The method has been specifically designed from the beginning to take advantage of the GPU's capacity to perform massively parallel calculations by not only executing a core set of mathematical calculations, but also running much of the underlying algorithmic logic on the GPU. In this study we present our three-dimensional model for in vivo diffusion that exploits the calculation capabilities of the GPU. The implementation of the diffusion operator on the GPU is subject to architectural constraints, and we discuss its structure and the trade-offs made to accommodate the GPU hardware.