DRAxML@home: a distributed program for computation of large phylogenetic trees

Inference of large phylogenetic trees using statistical methods is computationally extremely expensive. Thus, progress is primarily achieved via algorithmic innovation rather than by brute-force allocation of available computational ressources. We describe simple heuristics which yield accurate trees for synthetic (simulated) as well as real data and significantly improve execution time. The heuristics are implemented in a sequential program (RAxML) and a novel non-deterministic distributed algorithm (DRAxML@home). We implemented an MPI-based and a http-based distributed prototype of this algorithm and used DRAxML@home to infer trees comprising 1000 and 2025 organisms on LINUX PC clusters.