  Graphics Processing Units (GPUs) are high performance co-processors
originally intended to improve the use and quality of computer graphics
applications. Once, researchers and practitioners noticed the potential of
using GPU for general purposes, GPUs applications have been extended from
graphics applications to other fields. The main objective of this paper is to
evaluate the impact of using GPU in solution of the transient diffusion type
equation by parallel and stable group explicit finite difference method and
encourage the researchers in this field to immigrate from implementing their
algorithms in CPU to the GPU emerging world. For comparing them, we implemented
the method in both GPU and CPU (multi-core) programming context. Moreover, we
proposed an optimal synchronization arrangement for the implementation
pseudo-code. Also, the interrelation of GPU parallel programming and
initializing the algorithm variables were discussed, taking advantage of
numerical experiences. The GPU-approach results are faster than those obtained
from a much expensive parallel 8-thread CPU-based programming. The GPU used in
this paper, is an ordinary old laptop GPU (GT 335M, launched at 2010) and is
accessible for everyone and the newer generations of GPU (as discussed in
paper) have even more performance priority over the similar-price GPUs. Then,
the results are expected to encourage the entire research society to take
advantage of GPUs and improve the time efficiency of their studies.
