Open AccessParallel implementation of D‐Phylo algorithm for maximum likelihood clusters
Author(s) -
Malik Shamita,
Sharma Dolly,
Khatri Sunil Kumar
Publication year - 2017
Publication title -
iet nanobiotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 38
eISSN - 1751-875X
pISSN - 1751-8741
DOI - 10.1049/iet-nbt.2016.0005
Subject(s) - computer science , phylogenetic tree , constraint (computer aided design) , volume (thermodynamics) , algorithm , block (permutation group theory) , parallel computing , mathematics , combinatorics , biology , geometry , biochemistry , physics , quantum mechanics , gene
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 k ‐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.