Özet:

his paper describes a hybrid CPU/GPU approach for solving a two-phase mathematical model numerically. The dynamic of drug release between the first phase (coating) and second phase (arterial tissue) is represented by a system of partial differential equations (PDEs). The system of equations is discretized by Finite Element Method. The whole discretized system involves a large sparse system of equation which requires a high computation. The CPU/GPU approach provides a platform to solve PDEs having extensive computations in parallel. Consequently, this platform can significantly reduce the solution times as compared to the implementation of CPU. This allows for more efficient investigation of different mathematical models, as well as, the governing parameters. In this paper, a significant parallel computing framework is presented to solve the governing equations numerically using the Graphics Processing Units (GPUs) with CUDA. This two-phase model investigates the impact of key parameters related to mass concentrations and drug release from tissue and coating layers. The identification and the role of major parameters such as (Filtration velocity, the ratio of accessible void volume to solid volume, the solid-liquid mass transfer rate) are tinted. Furthermore, the motivation and guidance for using parallel computing in order to handle computational complexities and large sparse system arise after discretizing the model equations are explained. We have designed a hybrid CPU/GPU solution of the proposed model by using Matlab. The parallel performance results show that CPU/GPU architecture is more efficient in large-scale problem simulations.

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