eccCL: parallelized GPU implementation of Ensemble Classifier Chains

BMC Bioinformatics

Software

Mona Riemenschneider¹  Dominik Heider²  Ari Rasch³  Alexander Herbst³  Sergei Gorlatch³ 

[1] Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, 94315, Straubing, Germany;Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, 94315, Straubing, Germany;Wissenschaftszentrum Weihenstephan, Technische Universität München, Alte Akademie 8, 85354, Freising, Germany;Present Address: Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, 35032, Marburg, Germany;Institute of Computer Science, University of Münster, Einsteinstr. 62, 48149, Münster, Germany;

关键词: Classifier chains;    Multi label classification;    High performance computing;   

DOI  :  10.1186/s12859-017-1783-9

 received in 2016-11-02, accepted in 2017-08-08,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundMulti-label classification has recently gained great attention in diverse fields of research, e.g., in biomedical application such as protein function prediction or drug resistance testing in HIV. In this context, the concept of Classifier Chains has been shown to improve prediction accuracy, especially when applied as Ensemble Classifier Chains. However, these techniques lack computational efficiency when applied on large amounts of data, e.g., derived from next-generation sequencing experiments. By adapting algorithms for the use of graphics processing units, computational efficiency can be greatly improved due to parallelization of computations.ResultsHere, we provide a parallelized and optimized graphics processing unit implementation (eccCL) of Classifier Chains and Ensemble Classifier Chains. Additionally to the OpenCL implementation, we provide an R-Package with an easy to use R-interface for parallelized graphics processing unit usage.ConclusioneccCL is a handy implementation of Classifier Chains on GPUs, which is able to process up to over 25,000 instances per second, and thus can be used efficiently in high-throughput experiments. The software is available at http://www.heiderlab.de.

【 授权许可】

CC BY   
© The Author(s) 2017