The Resource A distributed CPU-GPU framework for large-scale pairwise alignment, by Da Li

A distributed CPU-GPU framework for large-scale pairwise alignment, by Da Li

Label
A distributed CPU-GPU framework for large-scale pairwise alignment
Title
A distributed CPU-GPU framework for large-scale pairwise alignment
Statement of responsibility
by Da Li
Title variation
Distributed central processing unit-graphic processing unit framework for large-scale pairwise alignment
Creator
Contributor
Author
Thesis advisor
Subject
Genre
Language
eng
Summary
Several problems in computational biology require the all-against-all pairwise comparisons of tens of thousands of individual biological sequences. Each such comparison can be performed with the well-known Needleman-Wunsch alignment algorithm. However, with the rapid growth of biological databases, performing all possible comparisons with this algorithm in serial becomes extremely time-consuming. The massive computational power of graphics processing units (GPUs) makes them an appealing choice for accelerating these computations. As such, CPU-GPU clusters can enable all-against-all comparisons on large datasets. This thesis presents a hybrid MPI-CUDA framework for computing multiple pairwise sequence alignments on CPU-GPU clusters. The design targets both homogeneous and heterogeneous clusters with nodes characterized by different hardware and computing capabilities. The framework consists of the following components: a cluster-level dispatcher, a set of node-level dispatchers, and a set of CPU- and GPU-workers. The cluster-level dispatcher progressively distributes work to the compute nodes and aggregates the results. The node-level dispatchers distribute alignment tasks to available CPUs and GPUs and perform dual-buffering to hide data transfers between CPU and GPU. CPU- and GPU-workers perform pairwise sequence alignments using the Needleman-Wunsch algorithm. The proposed GPU workers are evaluated on different platforms and all of them outperform the existing open-source implementation from the Rodinia Benchmark Suite
Cataloging source
MUU
http://library.link/vocab/creatorName
Li, Da
Degree
M.S.
Dissertation note
Thesis
Dissertation year
2014.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorName
Becchi, Michela
http://library.link/vocab/subjectName
  • CUDA (Computer architecture)
  • Big data
  • Cluster analysis
Label
A distributed CPU-GPU framework for large-scale pairwise alignment, by Da Li
Instantiates
Publication
Note
  • "MAY 2014."
  • "A Thesis Presented to the Faculty of the Graduate School at the University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."
  • Thesis supervisor: Dr. Michela Becchi
Accompanying material
2 supplementary files
Bibliography note
Includes bibliographical references (pages 44-47)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
956315975
Extent
1 online resource (vii, 47 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (chiefly color) +
Specific material designation
remote
System control number
(OCoLC)956315975
Label
A distributed CPU-GPU framework for large-scale pairwise alignment, by Da Li
Publication
Note
  • "MAY 2014."
  • "A Thesis Presented to the Faculty of the Graduate School at the University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."
  • Thesis supervisor: Dr. Michela Becchi
Accompanying material
2 supplementary files
Bibliography note
Includes bibliographical references (pages 44-47)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
956315975
Extent
1 online resource (vii, 47 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (chiefly color) +
Specific material designation
remote
System control number
(OCoLC)956315975

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