CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000294782100021&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1109/TCBB.2011.33
eISSN
1557-9964
Externe Identifier
  • Clarivate Analytics Document Solution ID: 818UU
  • PubMed Identifier: 21339531
ISSN
1545-5963
Ausgabe der Veröffentlichung
6
Zeitschrift
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Schlüsselwörter
  • BLAST
  • dynamic programming
  • sequence alignment
  • graphics hardware
  • GPGPU
  • CUDA
Paginierung
1678 - 1684
Datum der Veröffentlichung
2011
Status
Published
Titel
CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware
Sub types
  • Article
Ausgabe der Zeitschrift
8

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Autoren
DOI
10.1109/tcbb.2011.33
ISSN
1545-5963
Ausgabe der Veröffentlichung
6
Zeitschrift
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Paginierung
1678 - 1684
Datum der Veröffentlichung
2011
Status
Published
Herausgeber
Institute of Electrical and Electronics Engineers (IEEE)
Herausgeber URL
http://dx.doi.org/10.1109/tcbb.2011.33
Datum der Datenerfassung
2021
Titel
CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware
Ausgabe der Zeitschrift
8

Datenquelle: Crossref

Abstract
Scanning protein sequence database is an often repeated task in computational biology and bioinformatics. However, scanning large protein databases, such as GenBank, with popular tools such as BLASTP requires long runtimes on sequential architectures. Due to the continuing rapid growth of sequence databases, there is a high demand to accelerate this task. In this paper, we demonstrate how GPUs, powered by the Compute Unified Device Architecture (CUDA), can be used as an efficient computational platform to accelerate the BLASTP algorithm. In order to exploit the GPU’s capabilities for accelerating BLASTP, we have used a compressed deterministic finite state automaton for hit detection as well as a hybrid parallelization scheme. Our implementation achieves speedups up to 10.0 on an NVIDIA GeForce GTX 295 GPU compared to the sequential NCBI BLASTP 2.2.22. CUDA-BLASTP source code which is available at https://sites.google.com/site/liuweiguohome/software.
Addresses
  • Fraunhofer IDM@NTU, Nanyang Technological University, NS-05-01, Singapore 639798. liuweiguo@ntu.edu.sg
Autoren
DOI
10.1109/tcbb.2011.33
eISSN
1557-9964
Externe Identifier
  • PubMed Identifier: 21339531
Open access
false
ISSN
1545-5963
Ausgabe der Veröffentlichung
6
Zeitschrift
IEEE/ACM transactions on computational biology and bioinformatics
Schlüsselwörter
  • Proteins
  • Algorithms
  • Computer Graphics
  • Software
  • Databases, Protein
Sprache
eng
Medium
Print
Paginierung
1678 - 1684
Datum der Veröffentlichung
2011
Status
Published
Datum der Datenerfassung
2011
Titel
CUDA-BLASTP: accelerating BLASTP on CUDA-enabled graphics hardware.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
8

Datenquelle: Europe PubMed Central

Abstract
Scanning protein sequence database is an often repeated task in computational biology and bioinformatics. However, scanning large protein databases, such as GenBank, with popular tools such as BLASTP requires long runtimes on sequential architectures. Due to the continuing rapid growth of sequence databases, there is a high demand to accelerate this task. In this paper, we demonstrate how GPUs, powered by the Compute Unified Device Architecture (CUDA), can be used as an efficient computational platform to accelerate the BLASTP algorithm. In order to exploit the GPU’s capabilities for accelerating BLASTP, we have used a compressed deterministic finite state automaton for hit detection as well as a hybrid parallelization scheme. Our implementation achieves speedups up to 10.0 on an NVIDIA GeForce GTX 295 GPU compared to the sequential NCBI BLASTP 2.2.22. CUDA-BLASTP source code which is available at https://sites.google.com/site/liuweiguohome/software.
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/21339531
DOI
10.1109/TCBB.2011.33
eISSN
1557-9964
Ausgabe der Veröffentlichung
6
Zeitschrift
IEEE/ACM Trans Comput Biol Bioinform
Schlüsselwörter
  • Algorithms
  • Computer Graphics
  • Databases, Protein
  • Proteins
  • Software
Sprache
eng
Country
United States
Paginierung
1678 - 1684
Datum der Veröffentlichung
2011
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2012
Titel
CUDA-BLASTP: accelerating BLASTP on CUDA-enabled graphics hardware.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
8

Datenquelle: PubMed

Autoren
Zeitschrift
IEEE ACM Trans. Comput. Biol. Bioinform.
Artikelnummer
6
Paginierung
1678 - 1684
Datum der Veröffentlichung
2011
Titel
CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware.
Ausgabe der Zeitschrift
8

Datenquelle: DBLP

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