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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hartigan, J.A., Wong, M.A.: A K-Means clustering algorithm. Appl. Stat. 28, 126–130 (1979)
Xiao, Y., Leung, C.S., Ho, T.Y., Lam, P.M.: A GPU implementation for LBG and SOM training. Neural Computing and Applications (2010)
Ernst, J., et al.: Clustering short time series gene expression data. Bioinformatics 21, 159–168 (2005)
Takizawa, H., Kobayashi, H.: Hierarchical parallel processing of large scale data clustering on a pc cluster with gpu co-processing. J. Supercomput. 36, 219–234 (2006)
Troyanskaya, O., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001)
Chu, S., et al.: The transcriptional program of sporulation in budding yeast. Science 282, 699–705 (1998)
Guillemin, K., Salama, N., Tompkins, L., Falkow, S.: Cag pathogenicity island-specific responses of gastric epithelial cells to Helicobacter pylori infection. Proc. Natl. Acad. Sci. USA 99, 15136–15141 (2002)
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lam, YK., Situ, W., Tsang, P.W.M., Leung, CS., Xiao, Y. (2011). Comparison between the Applications of Fragment-Based and Vertex-Based GPU Approaches in K-Means Clustering of Time Series Gene Expression Data. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_78
Download citation
DOI: https://doi.org/10.1007/978-3-642-24955-6_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
Online ISBN: 978-3-642-24955-6
eBook Packages: Computer ScienceComputer Science (R0)