• Login
    View Item 
    •   Home
    • Brock Theses
    • Masters Theses
    • M.Sc. Computer Science
    • View Item
    •   Home
    • Brock Theses
    • Masters Theses
    • M.Sc. Computer Science
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of BrockUCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjectsProfilesView

    My Account

    LoginRegister

    Statistics

    Display statistics

    IMPROVING BWA-MEM WITH GPU PARALLEL COMPUTING

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Brock_Li_Connor_2021.pdf
    Size:
    6.098Mb
    Format:
    PDF
    Download
    Author
    Li, Connor
    Keyword
    BWA-MEM
    GPGPU
    Big Data
    Spark
    
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10464/15019
    Abstract
    Due to the many advances made in designing algorithms, especially the ones used in bioinformatics, it is becoming harder and harder to improve their efficiencies. Therefore, hardware acceleration using General-Purpose computing on Graphics Processing Unit has become a popular choice. BWA-MEM is an important part of the BWA software package for sequence mapping. Because of its high speed and accuracy, we choose to parallelize the popular short DNA sequence mapper. BWA has been a prevalent single node tool in genome alignment, and it has been widely studied for acceleration for a long time since the first version of the BWA package came out. This thesis presents the Big Data GPGPU distributed BWA-MEM, a tool that combines GPGPU acceleration and distributed computing. The four hardware parallelization techniques used are CPU multi-threading, GPU paralleled, CPU distributed, and GPU distributed. The GPGPU distributed software typically outperforms other parallelization versions. The alignment is performed on a distributed network, and each node in the network executes a separate GPGPU paralleled version of the software. We parallelize the chain2aln function in three levels. In Level 1, the function ksw\_extend2, an algorithm based on Smith-Waterman, is parallelized to handle extension on one side of the seed. In Level 2, the function chain2aln is parallelized to handle chain extension, where all seeds within the same chain are extended. In Level 3, part of the function mem\_align1\_core is parallelized for extending multiple chains. Due to the program's complexity, the parallelization work was limited at the GPU version of ksw\_extend2 parallelization Level 3. However, we have successfully combined Spark with BWA-MEM and ksw\_extend2 at parallelization Level 1, which has shown that the proposed framework is possible. The paralleled Level 3 GPU version of ksw\_extend2 demonstrated noticeable speed improvement with the test data set.
    Collections
    M.Sc. Computer Science

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.