Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the maximum likelihood method is computationally extremely expensive. In previous work, we have introduced subtree equality vectors (SEV) to significantly reduce the number of required floating point operations during topology evaluation and implemented this method in (P)AxML, which is a derivative of (parallel) fastDNAml. Experimental results show that (P)AxML scales particularly well on inexpensive PC-processor architectures obtaining global run time accelerations between 51% and 65% over (parallel) fastDNAml for large data sets, yet rendering exactly the same output. In this paper, we present an additional SEV-based algorithmic optimization which scales well on PC processors and leads to a further improvement of global execution times of 14% to 19% compared to the initial version of AxML. Furthermore, we present novel distance-based heuristics for reducing the number of analyzed tree topologies, which further accelerate the program by 4% up to 8%. Finally, we discuss a novel experimental tree-building algorithm and potential heuristic solutions for inferring large high quality trees, which for some initial tests rendered better trees and accelerated program execution at the same time by a factor greater than 6

@inproceedings{PTIOPAWASL03,
	author	 = {Alexandros P. Stamatakis and Thomas Ludwig},
	title	 = {{Phylogenetic tree inference on PC architectures with AxML/PAxML}},
	year	 = {2003},
	booktitle	 = {{Proceedings of the International Parallel and Distributed Processing Symposium}},
	publisher	 = {IEEE Computer Society},
	address	 = {Washington, DC, USA},
	pages	 = {8},
	conference	 = {IPDPS-03},
	organization	 = {University of Nice},
	location	 = {Nice, France},
	isbn	 = {0-7695-1926-1},
	doi	 = {http://dx.doi.org/10.1109/IPDPS.2003.1213296},
	abstract	 = {Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the maximum likelihood method is computationally extremely expensive. In previous work, we have introduced subtree equality vectors (SEV) to significantly reduce the number of required floating point operations during topology evaluation and implemented this method in (P)AxML, which is a derivative of (parallel) fastDNAml. Experimental results show that (P)AxML scales particularly well on inexpensive PC-processor architectures obtaining global run time accelerations between 51\% and 65\% over (parallel) fastDNAml for large data sets, yet rendering exactly the same output. In this paper, we present an additional SEV-based algorithmic optimization which scales well on PC processors and leads to a further improvement of global execution times of 14\% to 19\% compared to the initial version of AxML. Furthermore, we present novel distance-based heuristics for reducing the number of analyzed tree topologies, which further accelerate the program by 4\% up to 8\%. Finally, we discuss a novel experimental tree-building algorithm and potential heuristic solutions for inferring large high quality trees, which for some initial tests rendered better trees and accelerated program execution at the same time by a factor greater than 6},
}