Abstract
Multiple sequence alignment is an important tool to represent similarities among biological sequences and it allows obtaining relevant information such as evolutionary history, among others. Due to its importance, several methods have been proposed to the problem. However, the inherent complexity of the problem allows only non-exact solutions and further for small length sequences or few sequences. Hence, the scenario of rapid increment of the sequence databases leads to prohibitive runtimes for large-scale sequence datasets. In this work we describe a Multi-GPU approach for the three stages of the Progressive Alignment method which allow to address a large number of lengthy sequence alignments in reasonable time. We compare our results with two popular aligners ClustalW-MPI and Clustal\(\varOmega \) and with CUDA NW module of the Rodinia Suite. Our proposal with 8 GPUs achieved speedups ranging from 28.5 to 282.6 with regard to ClustalW-MPI with 32 CPUs considering NCBI and synthetic datasets. When compared to Clustal\(\varOmega \) with 32 CPUs for NCBI and synthetic datasets we had speedups between 3.3 and 32. In comparison with CUDA NW_Rodinia the speedups range from 155 to 830 considering all scenarios.
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Acknowledgments
We thank the High Performance Computing Center (NPAD/UFRN) and CTEI/UFMS for providing computational resources, and grants #2018/18560-6, #2018/21934-5, São Paulo Research Foundation (FAPESP) for financial support.
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de O. Siqueira, R.A., Stefanes, M.A., Rozante, L.C.S., Martins-Jr, D.C., de Souza, J.E.S., Araujo, E. (2021). Multi-GPU Approach for Large-Scale Multiple Sequence Alignment. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12949. Springer, Cham. https://doi.org/10.1007/978-3-030-86653-2_41
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