Publications - Lucas Beyer

Peer Reviewed Conference Publication

Streaming Data from HDD to GPUs for Sustained Peak Performance
Lucas Beyer and Paolo Bientinesi
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",
    doi       = "10.1007/978-3-642-40047-6_78",
    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

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"
}

web PDF bibtex

Technical Report

Streaming Data from HDD to GPUs for Sustained Peak Performance
Lucas Beyer and Paolo Bientinesi
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",
    url         = "https://arxiv.org/pdf/1302.4332"
}
```
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.
abstractPDFbibtexhide