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      • Electronic Theses and Dissertations
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      • HARVEST
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      MR-CUDASW - GPU accelerated Smith-Waterman algorithm for medium-length (meta)genomic data

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      MUHAMMADZADEH-THESIS.pdf (4.260Mb)
      Date
      2015-01-08
      Author
      Muhammadzadeh, Amir
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      The idea of using a graphics processing unit (GPU) for more than simply graphic output purposes has been around for quite some time in scientific communities. However, it is only recently that its benefits for a range of bioinformatics and life sciences compute-intensive tasks has been recognized. This thesis investigates the possibility of improving the performance of the overlap determination stage of an Overlap Layout Consensus (OLC)-based assembler by using a GPU-based implementation of the Smith-Waterman algorithm. In this thesis an existing GPU-accelerated sequence alignment algorithm is adapted and expanded to reduce its completion time. A number of improvements and changes are made to the original software. Workload distribution, query profile construction, and thread scheduling techniques implemented by the original program are replaced by custom methods specifically designed to handle medium-length reads. Accordingly, this algorithm is the first highly parallel solution that has been specifically optimized to process medium-length nucleotide reads (DNA/RNA) from modern sequencing machines (i.e. Ion Torrent). Results show that the software reaches up to 82 GCUPS (Giga Cell Updates Per Second) on a single-GPU graphic card running on a commodity desktop hardware. As a result it is the fastest GPU-based implemen- tation of the Smith-Waterman algorithm tailored for processing medium-length nucleotide reads. Despite being designed for performing the Smith-Waterman algorithm on medium-length nucleotide sequences, this program also presents great potential for improving heterogeneous computing with CUDA-enabled GPUs in general and is expected to make contributions to other research problems that require sensitive pairwise alignment to be applied to a large number of reads. Our results show that it is possible to improve the performance of bioinformatics algorithms by taking full advantage of the compute resources of the underlying commodity hardware and further, these results are especially encouraging since GPU performance grows faster than multi-core CPUs.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Kusalik, Anthony J.
      Committee
      McQuillan, Ian; Keil, Mark; Rueda, Luis G.
      Copyright Date
      November 2014
      URI
      http://hdl.handle.net/10388/ETD-2014-11-1878
      Subject
      Bioinformatics
      Sequence Alignment
      Smith-Waterman Algorithm
      GPU Computing
      CUDA
      Sequence Assembly
      Metagenomics
      Next-Generation-Sequencing
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