- author = "W. B. Langdon and Brian Yee Hong Lam",
- title = "Genetically Improved {BarraCUDA}",
- journal = "BioData Mining",
- year = "2017",
- volume = "20",
- number = "28",
- month = "2 " # aug,
- keywords = "genetic algorithms, genetic programming, genetic improvement, GPGPU, parallel computing, double-ended DNA sequence, nextgen NGS",
-
DOI = "
doi:10.1186/s13040-017-0149-1",
- size = "11 pages",
-
abstract = "Background:
BarraCUDA is an open source C program which uses the BWA algorithm in parallel with nVidia CUDA to align short next generation DNA sequences against a reference genome. Recently its source code was optimised using Genetic Improvement.
Results:
The genetically improved (GI) code is up to three times faster on short paired end reads from The 1000 Genomes Project and 60percent more accurate on a short BioPlanet.com GCAT alignment benchmark. GPGPU Barracuda running on a single K80 Tesla GPU can align short paired end nextgen sequences up to ten times faster than bwa on a 12 core CPU.
Conclusions:
The speed up was such that the GI version was adopted and has been regularly downloaded from SourceForge for more than 12 months.",
-
notes = "Replaces RN/15/03 \cite{Langdon:RN1503}
PMID: 28785314",
