A Fast Program for Phylogenetic Tree Inference with Maximum Likelihood

Inference of large phylogenetic trees using elaborate statistical models is computationally extremely intensive. Thus, progress is primarily achieved via algorithmic innovation rather than by brute-force allocation of all available computational resources. We present simple heuristics which yield accurate trees for synthetic (simulated) as well as real data and improve execution time compared to the currently fastest programs. The new heuristics are implemented in a sequential program (RAxML) which is available as open source code. Furthermore, we present a non-deterministic parallel version of our algorithm which in some cases yielded super-linear speedups for computations with 1000 organisms. We compare sequential RAxML performance with the currently fastest and most accurate programs for phylogenetic tree inference based on statistical methods using 50 synthetic alignments and 9 real-world alignments comprising up to 1000 sequences. RAxML outperforms those programs for real-world data in terms of speed and final likelihood values.