Parallel reconstruction of neighbor-joining trees for large multiple sequence alignments using CUDA

Computing large multiple protein sequence alignments using progressive alignment tools such as ClustalW requires several hours on state-of-the-art workstations. ClustalW uses a three-stage processing pipeline: (i) pairwise distance computation; (ii) phylogenetic tree reconstruction; and (iii) progressive multiple alignment computation. Previous work on accelerating ClustalW was mainly focused on parallelizing the first stage and achieved good speedups for a few hundred input sequences. However, if the input size grows to several thousand sequences, the second stage can dominate the overall runtime. In this paper, we present a new approach to accelerating this second stage using graphics processing units (GPUs). In order to derive an efficient mapping onto the GPU architecture, we present a parallelization of the neighbor-joining tree reconstruction algorithm using CUDA. Our experimental results show speedups of over 26× for large datasets compared to the sequential implementation.

• Type: Conference
• Year: 2009
• Where: IPPS
• Authors: Yongchao Liu, Bertil Schmidt, Douglas L. Maskell
• Keywords: ClustalW, Distributed And Parallel Computing, IPPS 2009, Progressive Multiple Alignment, Tree Reconstruction