@inproceedings{98841603a55349cc8cf7e4343112a9b8,
title = "Efficient parallel UPGMA algorithm based on multiple GPUs",
abstract = "A phylogenetic tree is used to present the evolutionary relationships among the interesting biological species based on the similarities in their genetic sequences. The UPGMA is one of the popular algorithms to construct a phylogenetic tree according to the distance matrix created by the pairwise distances among taxa. To solve the performance issue of the UPGMA, the implementation of the UPGMA method on a single GPU has been proposed. However, it is not capable of handling the large taxa set. This work describes a novel parallel UPGMA approach on multiple GPUs that is able to build a tree from extremely large datasets. The experimental results show that the proposed approach with 4 NVIDIA GTX 980 achieves an approximately × fold speedup over the implementation of UPGMA on CPU and GPU, respectively.",
keywords = "GPU, Multiple GPU, Parallel computing, Phylogenetic tree, UPGMA",
author = "Hung, {Che Lun} and Lin, {Chun Yuan} and Wu, {Fu Che} and Chan, {Yu Wei}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",
year = "2017",
month = jan,
day = "17",
doi = "10.1109/BIBM.2016.7822640",
language = "English",
series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "870--873",
editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, {Xiaohua Tony} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",
booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",
address = "美國",
}