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Graphics Processing Units (GPUs) are high performance co-processorsoriginally intended to improve the use and quality of computer graphicsapplications. Once, researchers and practitioners noticed the potential ofusing GPU for general purposes, GPUs applications have been extended fromgraphics applications to other fields. The main objective of this paper is toevaluate the impact of using GPU in solution of the transient diffusion typeequation by parallel and stable group explicit finite difference method andencourage the researchers in this field to immigrate from implementing theiralgorithms in CPU to the GPU emerging world. For comparing them, we implementedthe method in both GPU and CPU (multi-core) programming context. Moreover, weproposed an optimal synchronization arrangement for the implementationpseudo-code. Also, the interrelation of GPU parallel programming andinitializing the algorithm variables were discussed, taking advantage ofnumerical experiences. The GPU-approach results are faster than those obtainedfrom a much expensive parallel 8-thread CPU-based programming. The GPU used inthis paper, is an ordinary old laptop GPU (GT 335M, launched at 2010) and isaccessible for everyone and the newer generations of GPU (as discussed inpaper) have even more performance priority over the similar-price GPUs. Then,the results are expected to encourage the entire research society to takeadvantage of GPUs and improve the time efficiency of their studies.

GPU-Acceleration of Parallel Unconditionally Stable Group Explicit Finite Difference Method