| Abstract: |
| The graphic processing unit (GPU) with enormous arithmetic capability and streaming memory bandwidth is now a
powerful engine for scientific as well as industrial computing. We propose two parallel GPU algorithms, one for linear solver and the other for nonlinear solver, for solving the Poisson-Fermi equation approximated by the standard finite difference method in 3D to study biological ion channels with crystallized structures from the Protein Data Bank, for example. The results show that the parallel algorithms on GPU over the sequential algorithms on CPU (central processing unit) can achieve 22.8$\times $ and 16.9$\times$ speedups for the linear solver time and total runtime, respectively. |
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