GPU accelerated blood flow computation using the Lattice Boltzmann Method

We propose a numerical implementation based on a Graphics Processing Unit (GPU) for the acceleration of the execution time of the Lattice Boltzmann Method (LBM). The study focuses on the application of the LBM for patient-specific blood flow computations, and hence, to obtain higher accuracy, double precision computations are employed. The LBM specific operations are grouped into two kernels, whereas only one of them uses information from neighboring nodes. Since for blood flow computations regularly only 1/5 or less of the nodes represent fluid nodes, an indirect addressing scheme is used to reduce the memory requirements. Three GPU cards are evaluated with different 3D benchmark applications (Poisseuille flow, lid-driven cavity flow and flow in an elbow shaped domain) and the best performing card is used to compute blood flow in a patient-specific aorta geometry with coarctation. The speed-up over a multi-threaded CPU code is of 19.42x. The comparison with a basic GPU based LBM implementation demonstrates the importance of the optimization activities.

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