Comparison between the Applications of Fragment-Based and Vertex-Based GPU Approaches in K-Means Clustering of Time Series Gene Expression Data

Abstract

With the emergence of microarray technology, clustering of gene expression data has become an area of immense interest in recent years. However, due to the high dimensionality and complexity of the gene data landscape, the clustering process generally involves enormous amount of arithmetic operations. The problem has been partially alleviated with the K-Means algorithm, which enables high dimension data to be clustered efficiently. Further enhancement on the computation speed is achieved with the use of fragment shader running in a graphic processing unit (GPU) environment. Despite the success, such approach is not optimal as the process is scattered between the CPU and the GPU, causing bottleneck in the data exchange between the two processors, and the underused of the GPU. In this paper, we propose to realize the K-Means clustering algorithm with an integration of the vertex and the fragment shaders, which enables the majority of the clustering process to be implemented within the GPU. Experimental evaluation reflects that the computation efficiency of our proposed method in clustering short time gene expression is around 1.5 to 2 times faster than that attained with the conventional fragment shaders.