Langdon, WB;
Vilella, A;
Lam, BYH;
Petke, J;
Harman, M;
(2016)
Benchmarking genetically improved BarraCUDA on epigenetic methylation NGS datasets and nVidia GPUs.
In: Friedrich, T and Neumann, F and Sutton, AM, (eds.)
GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion.
(pp. pp. 1131-1132).
Association for Computing Machinery (ACM): New York, NY, USA.
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Abstract
BarraCUDA uses CUDA graphics cards to map DNA reads to the human genome. Previously its software source code was genetically improved for short paired end next generation sequences. On longer noisy epigenetics strings using nVidia Titan and twin Tesla K40 the same GI-ed code is more than 3 times faster than bwa-meth on an 8 core CPU.
Type: | Proceedings paper |
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Title: | Benchmarking genetically improved BarraCUDA on epigenetic methylation NGS datasets and nVidia GPUs |
Event: | GECCO '16: Genetic and Evolutionary Computation Conference, 20-24 July 2016, Denver, CO, USA |
ISBN-13: | 9781450343237 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1145/2908961.2931687 |
Publisher version: | http://dx.doi.org/10.1145/2908961.2931687 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Genetic programming; Genetic Improvement; GPGPU; SBSE; parallel processing; bisulfite conversion; Bioinformatics |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1521866 |
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