DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000289278300001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1186/1471-2105-12-85
Externe Identifier
  • Clarivate Analytics Document Solution ID: 746WG
  • PubMed Identifier: 21447171
ISSN
1471-2105
Zeitschrift
BMC BIOINFORMATICS
Artikelnummer
ARTN 85
Datum der Veröffentlichung
2011
Status
Published
Titel
DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI
Sub types
  • Article
Ausgabe der Zeitschrift
12

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Autoren
DOI
10.1186/1471-2105-12-85
eISSN
1471-2105
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Bioinformatics
Sprache
en
Artikelnummer
85
Online publication date
2011
Datum der Veröffentlichung
2011
Status
Published
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1186/1471-2105-12-85
Datum der Datenerfassung
2019
Titel
DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI
Ausgabe der Zeitschrift
12

Datenquelle: Crossref

Abstract
<h4>Background</h4>Next-generation sequencing technologies have led to the high-throughput production of sequence data (reads) at low cost. However, these reads are significantly shorter and more error-prone than conventional Sanger shotgun reads. This poses a challenge for the de novo assembly in terms of assembly quality and scalability for large-scale short read datasets.<h4>Results</h4>We present DecGPU, the first parallel and distributed error correction algorithm for high-throughput short reads (HTSRs) using a hybrid combination of CUDA and MPI parallel programming models. DecGPU provides CPU-based and GPU-based versions, where the CPU-based version employs coarse-grained and fine-grained parallelism using the MPI and OpenMP parallel programming models, and the GPU-based version takes advantage of the CUDA and MPI parallel programming models and employs a hybrid CPU+GPU computing model to maximize the performance by overlapping the CPU and GPU computation. The distributed feature of our algorithm makes it feasible and flexible for the error correction of large-scale HTSR datasets. Using simulated and real datasets, our algorithm demonstrates superior performance, in terms of error correction quality and execution speed, to the existing error correction algorithms. Furthermore, when combined with Velvet and ABySS, the resulting DecGPU-Velvet and DecGPU-ABySS assemblers demonstrate the potential of our algorithm to improve de novo assembly quality for de-Bruijn-graph-based assemblers.<h4>Conclusions</h4>DecGPU is publicly available open-source software, written in CUDA C++ and MPI. The experimental results suggest that DecGPU is an effective and feasible error correction algorithm to tackle the flood of short reads produced by next-generation sequencing technologies.
Addresses
  • School of Computer Engineering, Nanyang Technological University, 639798, Singapore. liuy0039@ntu.edu.sg
Autoren
DOI
10.1186/1471-2105-12-85
eISSN
1471-2105
Externe Identifier
  • PubMed Identifier: 21447171
  • PubMed Central ID: PMC3072957
Open access
true
ISSN
1471-2105
Zeitschrift
BMC bioinformatics
Schlüsselwörter
  • Reproducibility of Results
  • Sequence Analysis, DNA
  • Algorithms
  • Computer Simulation
  • Software
Sprache
eng
Medium
Electronic
Online publication date
2011
Open access status
Open Access
Paginierung
85
Datum der Veröffentlichung
2011
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2011
Titel
DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI.
Sub types
  • data-paper
  • Journal Article
Ausgabe der Zeitschrift
12

Files

https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-12-85 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21447171/pdf/?tool=EBI http://www.biomedcentral.com/content/pdf/1471-2105-12-85.pdf https://europepmc.org/articles/PMC3072957?pdf=render

Datenquelle: Europe PubMed Central

Abstract
BACKGROUND: Next-generation sequencing technologies have led to the high-throughput production of sequence data (reads) at low cost. However, these reads are significantly shorter and more error-prone than conventional Sanger shotgun reads. This poses a challenge for the de novo assembly in terms of assembly quality and scalability for large-scale short read datasets. RESULTS: We present DecGPU, the first parallel and distributed error correction algorithm for high-throughput short reads (HTSRs) using a hybrid combination of CUDA and MPI parallel programming models. DecGPU provides CPU-based and GPU-based versions, where the CPU-based version employs coarse-grained and fine-grained parallelism using the MPI and OpenMP parallel programming models, and the GPU-based version takes advantage of the CUDA and MPI parallel programming models and employs a hybrid CPU+GPU computing model to maximize the performance by overlapping the CPU and GPU computation. The distributed feature of our algorithm makes it feasible and flexible for the error correction of large-scale HTSR datasets. Using simulated and real datasets, our algorithm demonstrates superior performance, in terms of error correction quality and execution speed, to the existing error correction algorithms. Furthermore, when combined with Velvet and ABySS, the resulting DecGPU-Velvet and DecGPU-ABySS assemblers demonstrate the potential of our algorithm to improve de novo assembly quality for de-Bruijn-graph-based assemblers. CONCLUSIONS: DecGPU is publicly available open-source software, written in CUDA C++ and MPI. The experimental results suggest that DecGPU is an effective and feasible error correction algorithm to tackle the flood of short reads produced by next-generation sequencing technologies.
Date of acceptance
2011
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/21447171
DOI
10.1186/1471-2105-12-85
eISSN
1471-2105
Externe Identifier
  • PubMed Central ID: PMC3072957
Zeitschrift
BMC Bioinformatics
Schlüsselwörter
  • Algorithms
  • Computer Simulation
  • Reproducibility of Results
  • Sequence Analysis, DNA
  • Software
Sprache
eng
Country
England
Paginierung
85
PII
1471-2105-12-85
Datum der Veröffentlichung
2011
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2012
Titel
DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
12

Datenquelle: PubMed

Autoren
Zeitschrift
BMC Bioinform.
Paginierung
85 - 85
Datum der Veröffentlichung
2011
Titel
DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI.
Ausgabe der Zeitschrift
12

Datenquelle: DBLP

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