A Fast Algorithm for the Computation and Enumeration of Perfect Phylogenies
SIAM Journal on Computing
Parallel implementation of DNAm1 program on message-passing architectures
Parallel Computing
Theoretical Computer Science
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Parallel algorithms for Bayesian phylogenetic inference
Journal of Parallel and Distributed Computing - High-performance computational biology
A fast program for maximum likelihood-based inference of large phylogenetic trees
Proceedings of the 2004 ACM symposium on Applied computing
Phylogenetic analysis using maximum likelihood methods in homogeneous parallel environments
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
| Hi-index | 0.00 |
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 run time 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.