Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Haidong Lan
- Yuandong Chan
- Kai Xu
- Bertil Schmidt
- Shaoliang Peng
- Weiguo Liu
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000383659000004&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1186/s12859-016-1128-0
- Externe Identifier
- Clarivate Analytics Document Solution ID: DW5CF
- PubMed Identifier: 27455061
- ISSN
- 1471-2105
- Zeitschrift
- BMC BIOINFORMATICS
- Schlüsselwörter
- Smith-Waterman
- Dynamic programming
- Pairwise sequence alignment
- Multiple sequence alignment
- Xeon Phi clusters
- Artikelnummer
- ARTN 267
- Datum der Veröffentlichung
- 2016
- Status
- Published
- Titel
- Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters
- Sub types
- Article
- Proceedings Paper
- Ausgabe der Zeitschrift
- 17
Datenquelle: Web of Science (Lite)
- Andere Metadatenquellen:
-
- Autoren
- Haidong Lan
- Yuandong Chan
- Kai Xu
- Bertil Schmidt
- Shaoliang Peng
- Weiguo Liu
- DOI
- 10.1186/s12859-016-1128-0
- eISSN
- 1471-2105
- Ausgabe der Veröffentlichung
- S9
- Zeitschrift
- BMC Bioinformatics
- Sprache
- en
- Artikelnummer
- 267
- Online publication date
- 2016
- Datum der Veröffentlichung
- 2016
- Status
- Published
- Herausgeber
- Springer Science and Business Media LLC
- Herausgeber URL
- http://dx.doi.org/10.1186/s12859-016-1128-0
- Datum der Datenerfassung
- 2019
- Titel
- Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters
- Ausgabe der Zeitschrift
- 17
Datenquelle: Crossref
- Abstract
- <h4>Background</h4>Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators.<h4>Results</h4>This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency.<h4>Conclusions</h4>Evaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi .
- Addresses
- School of Computer Science and Technology, Shandong University, Shunhua Road 1500, Jinan, Shandong, China.
- Autoren
- Haidong Lan
- Yuandong Chan
- Kai Xu
- Bertil Schmidt
- Shaoliang Peng
- Weiguo Liu
- DOI
- 10.1186/s12859-016-1128-0
- eISSN
- 1471-2105
- Externe Identifier
- PubMed Identifier: 27455061
- PubMed Central ID: PMC4959381
- Open access
- true
- ISSN
- 1471-2105
- Zeitschrift
- BMC bioinformatics
- Schlüsselwörter
- Proteins
- Sequence Alignment
- Computational Biology
- Amino Acid Sequence
- Algorithms
- Software
- Databases, Nucleic Acid
- Databases, Protein
- Sprache
- eng
- Medium
- Electronic
- Online publication date
- 2016
- Open access status
- Open Access
- Paginierung
- 267
- Datum der Veröffentlichung
- 2016
- Status
- Published
- Publisher licence
- CC BY
- Datum der Datenerfassung
- 2016
- Titel
- Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.
- Sub types
- research-article
- Journal Article
- Ausgabe der Zeitschrift
- 17 Suppl 9
Files
https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1128-0 https://europepmc.org/articles/PMC4959381?pdf=render
Datenquelle: Europe PubMed Central
- Abstract
- BACKGROUND: Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators. RESULTS: This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency. CONCLUSIONS: Evaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi .
- Autoren
- Haidong Lan
- Yuandong Chan
- Kai Xu
- Bertil Schmidt
- Shaoliang Peng
- Weiguo Liu
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/27455061
- DOI
- 10.1186/s12859-016-1128-0
- eISSN
- 1471-2105
- Externe Identifier
- PubMed Central ID: PMC4959381
- Ausgabe der Veröffentlichung
- Suppl 9
- Zeitschrift
- BMC Bioinformatics
- Schlüsselwörter
- Dynamic programming
- Multiple sequence alignment
- Pairwise sequence alignment
- Smith-Waterman
- Xeon Phi clusters
- Algorithms
- Amino Acid Sequence
- Computational Biology
- Databases, Nucleic Acid
- Databases, Protein
- Proteins
- Sequence Alignment
- Software
- Sprache
- eng
- Country
- England
- Paginierung
- 267
- PII
- 10.1186/s12859-016-1128-0
- Datum der Veröffentlichung
- 2016
- Status
- Published online
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2017
- Titel
- Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 17 Suppl 9
Datenquelle: PubMed
- Autoren
- Haidong Lan
- Yuandong Chan
- Kai Xu
- Bertil Schmidt
- Shaoliang Peng
- Weiguo Liu
- Zeitschrift
- BMC Bioinform.
- Artikelnummer
- S-9
- Paginierung
- 267 - 267
- Datum der Veröffentlichung
- 2016
- Titel
- Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.
- Ausgabe der Zeitschrift
- 17
Datenquelle: DBLP
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