Accelerating Parallel Maximum Likelihood-Based Phylogenetic Tree Calculations Using Subtree Equality Vectors

TL;DR: This paper describes and uses Subtree Equality Vectors (SEVs) to reduce the number of required floating point operations during topology evaluation and integrated these optimizations into various sequential programs and into parallel fastDNAml, one of the most common and efficient parallel programs for calculating large phylogenetic trees.

Abstract: Heuristics for calculating phylogenetic trees for a large sets of aligned rRNA sequences based on the maximum likelihood method are computationally expensive. The core of most parallel algorithms, which accounts for the greatest part of computation time, is the tree evaluation function, that calculates the likelihood value for each tree topology. This paper describes and uses Subtree Equality Vectors (SEVs) to reduce the number of required floating point operations during topology evaluation. We integrated our optimizations into various sequential programs and into parallel fastDNAml, one of the most common and efficient parallel programs for calculating large phylogenetic trees. Experimental results for our parallel program, which renders exactly the same output as parallel fastDNAml show global runtime improvements of 26% to 65%. The optimization scales best on clusters of PCs, which also implies a substantial cost saving factor for the determination of large trees.