research-article

The High Performance Computing Applications for Bioinformatics Research

Authors:

Ming XiaoAuthors Info & Claims

ICBBS '17: Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

Pages 70 - 75

https://doi.org/10.1145/3121138.3121175

Published: 22 June 2017 Publication History

Abstract

This study reviews the current high performance computing applications for bioinformatics. Firstly, we introduce two popular high performance computing architecture such as single instruction multiple data (SIMD) and multiple instruction multiple data (MIMD). For SIMD, we employ CUDA as the example to show its popular applications. Here, we detail three CUDA related high performance computing applications for bioinformatics research, such as GPU-BLAST, CloudAligner and SEAL. For MIMD, we employ Hadoop as the example to show its popular applications. Here we detail three Hadoop related high performance computing applications for bioinformatics research, such as Clouldburst, SOAP3 and CLAST. Finally, we summarize the aim of the research.

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Cited By

Anderson TWheeler T(2024)An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov modelsBMC Bioinformatics10.1186/s12859-024-05879-325:1Online publication date: 29-Jul-2024
https://doi.org/10.1186/s12859-024-05879-3
Piña JOrozco-Arias STobón-Orozco NCamargo-Forero LTabares-Soto RGuyot R(2023)G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic EraEvolutionary Bioinformatics10.1177/1176934322115058519Online publication date: 20-Jan-2023
https://doi.org/10.1177/11769343221150585

Index Terms

The High Performance Computing Applications for Bioinformatics Research
1. Applied computing
  1. Life and medical sciences
    1. Consumer health

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ICBBS '17: Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

June 2017

184 pages

ISBN:9781450352222

DOI:10.1145/3121138

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Natl University of Singapore: National University of Singapore

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Published: 22 June 2017

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ICBBS '17

ICBBS '17: 6th International Conference on Bioinformatics and Biomedical Science

June 22 - 24, 2017

Singapore, Singapore

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Cited By

Anderson TWheeler T(2024)An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov modelsBMC Bioinformatics10.1186/s12859-024-05879-325:1Online publication date: 29-Jul-2024
https://doi.org/10.1186/s12859-024-05879-3
Piña JOrozco-Arias STobón-Orozco NCamargo-Forero LTabares-Soto RGuyot R(2023)G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic EraEvolutionary Bioinformatics10.1177/1176934322115058519Online publication date: 20-Jan-2023
https://doi.org/10.1177/11769343221150585

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