An Application Agglomerative clustering for Computer Graphics Processing

Computer graphics is the software efficacy which is majorly utilized in every social sector and it is depends on an existing as well as newly designed programs. An idea of computer graphics integrates the large number of current areas, from easy graphic drawing to the design of multiple images of actual existence, developing new projects based on the image by the utilization of programming tool. The hierarchical clustering is the most popular clustering algorithm which is utilized to understand the dataset structure. This proposes the utilization of high-end computer graphics to for performing the Agglomerative Hierarchical Clustering (AHC) by Compute Unified Device Architecture (CUDA) of NVIDIA. This research identified the capabilities of CUDA as well as proposed method to effectively control the data with the graphics hardware of data intense, independent, iterative or tedious persistence approaches like AHC. This research efficiently describes the greater parallel as well as internally distributed CUDA program structure. The experimental results show the proposed method attains the improved speed of 25 to 60 times against the CPU implementation by the utilization micro array gene expressions.