Publications - Lucas Beyer

Peer Reviewed Conference Publication

  1. Streaming Data from HDD to GPUs for Sustained Peak Performance
    Proceedings of the Euro-Par 2013, 19th International European Conference on Parallel and Distributed Computing, Lecture Notes in Computer Science, Volume 8097, pp. 788-799, Springer Berlin Heidelberg, May 2013.
    @inproceedings{Beyer2013:618,
        author    = "Lucas Beyer and Paolo Bientinesi",
        title     = "Streaming Data from HDD to GPUs for Sustained Peak Performance",
        year      = 2013,
        volume    = 8097,
        series    = "Lecture Notes in Computer Science",
        pages     = "788-799",
        month     = may,
        publisher = "Springer Berlin Heidelberg",
        url       = "http://arxiv.org/pdf/1302.4332v1.pdf"
    }
    In the context of the genome-wide association studies (GWAS), one has to solve long sequences of generalized least-squares problems; such a task has two limiting factors: execution time --often in the range of days or weeks-- and data management --data sets in the order of Terabytes. We present an algorithm that obviates both issues. By pipelining the computation, and thanks to a sophisticated transfer strategy, we stream data from hard disk to main memory to GPUs and achieve sustained peak performance; with respect to a highly-optimized CPU implementation, our algorithm shows a speedup of 2.6x. Moreover, the approach lends itself to multiple GPUs and attains almost perfect scalability. When using 4 GPUs, we observe speedups of 9x over the aforementioned implementation, and 488x over a widespread biology library.
    abstractwebPDFbibtexhide

Thesis

  1. Exploiting Graphics Accelerators for Computational Biology
    Aachen Institute for Computational Engineering Science, RWTH Aachen, June 2012.
    @mastersthesis{Beyer2012:400,
        author = "Lucas Beyer",
        title  = "Exploiting Graphics Accelerators for Computational Biology",
        school = "Aachen Institute for Computational Engineering Science, RWTH Aachen",
        year   = 2012,
        month  = jun,
        url    = "http://www.aices.rwth-aachen.de:8080/aices/preprint/documents/AICES-2012-06-01.pdf"
    }
    webPDFbibtexhide

Technical Report

  1. Streaming Data from HDD to GPUs for Sustained Peak Performance
    Aachen Institute for Computational Engineering Science, RWTH Aachen, February 2013.
    Technical Report AICES-2013/02-1.
    @techreport{Beyer2013:398,
        author      = "Lucas Beyer and Paolo Bientinesi",
        title       = "Streaming Data from HDD to GPUs for Sustained Peak Performance",
        institution = "Aachen Institute for Computational Engineering Science, RWTH Aachen",
        year        = 2013,
        month       = feb,
        note        = "Technical Report AICES-2013/02-1"
    }
    In the context of the genome-wide association studies (GWAS), one has to solve long sequences of generalized least-squares problems; such a task has two limiting factors: execution time --often in the range of days or weeks-- and data management --data sets in the order of Terabytes. We present an algorithm that obviates both issues. By pipelining the computation, and thanks to a sophisticated transfer strategy, we stream data from hard disk to main memory to GPUs and achieve sustained peak performance; with respect to a highly-optimized CPU implementation, our algorithm shows a speedup of 2.6x. Moreover, the approach lends itself to multiple GPUs and attains almost perfect scalability. When using 4 GPUs, we observe speedups of 9x over the aforementioned implementation, and 488x over a widespread biology library.
    abstractwebbibtexhide