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Benchmarking genetically improved BarraCUDA on epigenetic methylation NGS datasets and nVidia GPUs

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. Green open access

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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
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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