{"title":"A multi-threaded DNA tag/anti-tag library generator for multi-core platforms","authors":"A. Ravindran, Daniel J. Burns","doi":"10.1109/CIBCB.2009.4925724","DOIUrl":null,"url":null,"abstract":"This paper describes a new approach to the problem of generating DNA tag/anti-tag libraries for use in biological assay methods. This approach couples multi-threaded coding methods and a highly parallel multi-population genetic algorithm to leverage performance gains made possible by the multi-core CPUs increasingly prevalent in today's commodity computers. We also describe the results of experiments characterizing the performance of this approach, which can yield up to an 8X speedup on a workstation equipped with dual quad-core CPUs. We observe that the coding effort required to implement this approach using the C language and Pthreads parallel programming model is greatly reduced compared to previous methods using both the VHDL language and reconfigurable hardware (FPGAs), and compared to C with the MPI API run on a cluster of computers.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIBCB.2009.4925724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper describes a new approach to the problem of generating DNA tag/anti-tag libraries for use in biological assay methods. This approach couples multi-threaded coding methods and a highly parallel multi-population genetic algorithm to leverage performance gains made possible by the multi-core CPUs increasingly prevalent in today's commodity computers. We also describe the results of experiments characterizing the performance of this approach, which can yield up to an 8X speedup on a workstation equipped with dual quad-core CPUs. We observe that the coding effort required to implement this approach using the C language and Pthreads parallel programming model is greatly reduced compared to previous methods using both the VHDL language and reconfigurable hardware (FPGAs), and compared to C with the MPI API run on a cluster of computers.