Open AccessGPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering
Author(s) -
Shuji Suzuki,
Masanori Kakuta,
Takashi Ishida,
Yutaka Akiyama
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0157338
Subject(s) - computer science , parallel computing , cuda , general purpose computing on graphics processing units , sequence database , subsequence , cluster analysis , central processing unit , graphics , sequence alignment , computational science , computer graphics (images) , operating system , peptide sequence , artificial intelligence , mathematics , biology , mathematical analysis , biochemistry , bounded function , gene
Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ, which is a state-of-the-art homology search algorithm for protein sequences, onto a GPU and implemented it as GHOSTZ-GPU. In addition, we optimized memory access for GPU calculations and for communication between the CPU and GPU. As per results of the evaluation test involving metagenomic data, GHOSTZ-GPU with 12 CPU threads and 1 GPU was approximately 3.0- to 4.1-fold faster than GHOSTZ with 12 CPU threads. Moreover, GHOSTZ-GPU with 12 CPU threads and 3 GPUs was approximately 5.8- to 7.7-fold faster than GHOSTZ with 12 CPU threads.