提出一种基于Yarn云平台的基因启发式多序列比对算法。建立核酸替换等价矩阵作为基因启发式数学模型,构建Yarn云平台逻辑架构,通过对基因数据预处理、基因数据存储、基因序列比对、基因数据管理、基因数据分析等步骤,对数据分类保存,划...提出一种基于Yarn云平台的基因启发式多序列比对算法。建立核酸替换等价矩阵作为基因启发式数学模型,构建Yarn云平台逻辑架构,通过对基因数据预处理、基因数据存储、基因序列比对、基因数据管理、基因数据分析等步骤,对数据分类保存,划分错误率较高的长序列,得到多个较短的基因片段。对不同片段实施定位,将其中的变长种子生成,进行骨架构建和孔隙填补,可以实现基因启发式多序列比对。结果表明,设计的算法在不同数据集下处理时间缩短,多序列比对SP(Sum of Pairs)的分值较高,实验验证了该多序列比对方法具有很好的应用价值。展开更多
This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s ...This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s thesis project ＂Optimization of complex tasks＇ computation on hybrid distributed computational structures＂ accomplished by Orekhov during which the main research objective was the determination of＂ patterns of the behavior of scaling efficiency and other parameters which define performance of different algorithms＇ implementations executed on hybrid distributed computational structures. Major outcomes and dependencies obtained within the master＇s thesis project were formed into a methodology which covers the problems of applications based on parallel computations and describes the process of its development in details, offering easy ways of avoiding potentially crucial problems. The paper is backed by the real-life examples such as clustering algorithms instead of artificial benchmarks.展开更多
文摘提出一种基于Yarn云平台的基因启发式多序列比对算法。建立核酸替换等价矩阵作为基因启发式数学模型,构建Yarn云平台逻辑架构,通过对基因数据预处理、基因数据存储、基因序列比对、基因数据管理、基因数据分析等步骤,对数据分类保存,划分错误率较高的长序列,得到多个较短的基因片段。对不同片段实施定位,将其中的变长种子生成,进行骨架构建和孔隙填补,可以实现基因启发式多序列比对。结果表明,设计的算法在不同数据集下处理时间缩短,多序列比对SP(Sum of Pairs)的分值较高,实验验证了该多序列比对方法具有很好的应用价值。
文摘This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s thesis project ＂Optimization of complex tasks＇ computation on hybrid distributed computational structures＂ accomplished by Orekhov during which the main research objective was the determination of＂ patterns of the behavior of scaling efficiency and other parameters which define performance of different algorithms＇ implementations executed on hybrid distributed computational structures. Major outcomes and dependencies obtained within the master＇s thesis project were formed into a methodology which covers the problems of applications based on parallel computations and describes the process of its development in details, offering easy ways of avoiding potentially crucial problems. The paper is backed by the real-life examples such as clustering algorithms instead of artificial benchmarks.
