Conformational entropy calculation, usually computed by normal mode analysis (NMA) or quasi harmonic analysis (QHA), is extremely time-consuming. Here, instead of NMA or QHA, a solvent accessible surface area (SASA) based model was employed to compute the conformational entropy, and a new fast GPU-based method called MURCIA (Molecular Unburied Rapid Calculation of Individual Areas) was implemented to accelerate the calculation of SASA for each atom. MURCIA employs two different kernels to determine the neighbours of each atom. The first kernel (K1) uses brute force for the calculation of the neighbours of atoms, while the second one (K2) uses an advanced algorithm involving hardware interpolations via GPU texture memory unit for such purpose. These two kernels yield very similar results. Each kernel has its own advantages depending on the protein size. K1 performs better than K2 when the size is small, and vice versa. The algorithm was extensively evaluated for three protein datasets, and achieves good results for all of them. This GPU-accelerated version is ~600 times faster than the former sequential algorithm when the number of the atoms in a protein is up to 105.