In pattern recognition and image processing feature extraction is one of essential parts as it facilitates the subsequent learning and generalization steps. Feature extraction aims to get the special characteristics of an object that can be applied to produce the desired information. One of commonly used algorithm for feature extraction is Gray Level Co-occurrence Matrix (GLCM). Though this algorithm produces a good result, the twelve Haralick texture features requires high computation if CPUs are used. The emergence of the Graphics Processing Unit (GPU) helps cope with all that requires high computation and running GLCM calculations in GPU is just a matter of seconds. To benefit greatly from thousands of CUDA cores, one should be able to appropriately implement GLCM algorithm. GPU for GLCM had been done in several studies and each offered various improvement in computation times. In this research, we propose another technique of implementing GLCM algorithm and it shows that the computation time of the twelve Haralick texture features is 450 times faster than using CPU.