A hierarchical clustering algorithm for MIMD architecture.
Hierarchical clustering is the most often used method for grouping similar patterns of gene expression data. A fundamental problem with existing implementations of this clustering method is the inability to handle large data sets within a reasonable time and memory resources. We propose a parallelized algorithm of hierarchical clustering to solve this problem. Our implementation on a multiple instruction multiple data (MIMD) architecture shows considerable reduction in computational time and inter-node communication overhead, especially for large data sets. We use the standard message passing library, message passing interface (MPI) for any MIMD systems.
Du
Zhihua
Z
BioInformatics Research Centre, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore. duzhihua@pmail.ntu.edu.sg
Lin
Feng
F
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data*; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data*; D013997:Time Factors; D014584:User-Computer Interface