ArticlePloS one2016
GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Speeding genomic island discovery through systematic design of reference database composition.PloS one · 2024Article
- Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Taxonomic and Gene Category Analyses of Subgingival Plaques from a Group of Japanese Individuals with and without Periodontitis.International journal of molecular sciences · 2021Article
- RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach.Bioinformatics (Oxford, England) · 2017Article
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Authors and funding
4 authors.
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Abstract
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.
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