Efficient parallel UPGMA algorithm based on multiple GPUs

Che Lun Hung, Chun Yuan Lin, Fu Che Wu, Yu Wei Chan

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
編輯Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
發行者Institute of Electrical and Electronics Engineers Inc.
頁面870-873
頁數4
ISBN(電子)9781509016105
DOIs
出版狀態Published - 17 1月 2017
事件2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China
持續時間: 15 12月 201618 12月 2016

出版系列

名字Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
國家/地區China
城市Shenzhen
期間15/12/1618/12/16

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