Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Our results also indicate that the new development of GPU architecture benefits the fluid dynamics computing significantly.Ĭomputational modeling of drug binding to proteins is an integral component of direct drug design. The parallelized cell-based AMR processes have achieved 2x speedup on GT9800 and 18x on Tesla C2050, which demonstrates that parallel running of the cell-based AMR method on GPU is feasible and efficient. With the optimization of configuring a larger L1 cache and adopting Shared Memory based atomic operations on the newer GPU C2050, an acceleration ratio of 20 is achieved. An acceleration ratio of 4 is obtained between the parallel code running on the old GPU GT9800 and the serial code running on E3-1230 V2.

It is found that results obtained by GPUs agree very well with the exact or experimental results in literature. Specifically, the AMR is processed with atomic operations to parallelize list operations, and null memory recycling is realized to improve the efficiency of memory utilization. For the first time, the cell-based adaptive mesh refinement (AMR) is fully implemented on GPU for the unstructured quadrilateral grid, which greatly reduces the frequency of data exchange between GPU and CPU. A GPU accelerated inviscid flow solver is developed on an unstructured quadrilateral grid in the present work.

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