Malik Shamita, Sharma Dolly, Khatri Sunil Kumar
Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India.
Computer Science and Engineering, Shiv Nadar University, Noida, Uttar Pradesh, India.
IET Nanobiotechnol. 2017 Mar;11(2):134-142. doi: 10.1049/iet-nbt.2016.0005.
This study explains a newly developed parallel algorithm for phylogenetic analysis of DNA sequences. The newly designed D-Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach. The D-Phylo while misusing the seeking capacity of -means keeps away from its real constraint of getting stuck at privately conserved motifs. The authors have tested the behaviour of D-Phylo on Amazon Linux Amazon Machine Image(Hardware Virtual Machine)i2.4xlarge, six central processing unit, 122 GiB memory, 8 800 Solid-state drive Elastic Block Store volume, high network performance up to 15 processors for several real-life datasets. Distributing the clusters evenly on all the processors provides us the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors.
本研究阐述了一种新开发的用于DNA序列系统发育分析的并行算法。新设计的D-Phylo是一种使用最大似然法进行系统发育分析的更先进算法。D-Phylo在误用均值搜索能力的同时，避免了陷入私有保守基序的实际限制。作者在亚马逊Linux亚马逊机器映像（硬件虚拟机）i2.4xlarge、六个中央处理器、122 GiB内存、8800固态硬盘弹性块存储卷、高达15个处理器的高网络性能上，针对几个实际数据集测试了D-Phylo的性能。如果有大量处理器，将集群均匀分布在所有处理器上使我们有能力实现近乎直接的速度提升。
