MGUPGMA: A Fast UPGMA Algorithm With Multiple Graphics Processing Units Using NCCL
| Autor: | Fu Che Wu, Che Lun Hung, Chuan Yi Tang, Yu Wei Chan, Guan Jie Hua, Chun-Yuan Lin |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Speedup Computer science lcsh:Evolution GPU 02 engineering and technology Review Set (abstract data type) 03 medical and health sciences lcsh:QH359-425 0202 electrical engineering electronic engineering information engineering Genetics Graphics Neighbor joining Ecology Evolution Behavior and Systematics 020203 distributed computing multiple GPUs Phylogenetic tree parallel computing Computer Science Applications 030104 developmental biology Large set (Ramsey theory) UPGMA Pairwise comparison Algorithm Arithmetic mean |
| Zdroj: | Evolutionary Bioinformatics Evolutionary Bioinformatics, Vol 13 (2017) |
| ISSN: | 1176-9343 |
| Popis: | A phylogenetic tree is a visual diagram of the relationship between a set of biological species. The scientists usually use it to analyze many characteristics of the species. The distance-matrix methods, such as Unweighted Pair Group Method with Arithmetic Mean and Neighbor Joining, construct a phylogenetic tree by calculating pairwise genetic distances between taxa. These methods have the computational performance issue. Although several new methods with high-performance hardware and frameworks have been proposed, the issue still exists. In this work, a novel parallel Unweighted Pair Group Method with Arithmetic Mean approach on multiple Graphics Processing Units is proposed to construct a phylogenetic tree from extremely large set of sequences. The experimental results present that the proposed approach on a DGX-1 server with 8 NVIDIA P100 graphic cards achieves approximately 3-fold to 7-fold speedup over the implementation of Unweighted Pair Group Method with Arithmetic Mean on a modern CPU and a single GPU, respectively. |
| Databáze: | OpenAIRE |
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