Novel Parallelization Schemes for Large-Scale Likelihood-based Phylogenetic Inference

The molecular data avalanche generated by novel wet-lab sequencing technologies allows for reconstructing phylogenies (evolutionary trees) using hundreds of complete genomes as input data. Therefore, scalable codes are required to infer trees on these data under likelihood-based models of molecular evolution. We recently introduced a checkpointable and scalable MPI-based code for this purpose called RAxML-Light and are currently using it for several real-world data analysis projects. It turned out that the scalability of RAxML-Light is nonetheless still limited because of the fork-join parallelization approach that is deployed. To this end, we introduce a novel, generally applicable, approach to computing the phylogenetic likelihood in parallel on whole-genome datasets and implement it in ExaML (Exascale Maximum Likelihood). ExaML executes up to 3.2 times faster than RAxML-Light because of the more efficient parallelization and communication scheme, while implementing exactly the same tree search algorithm. Moreover, the new parallelization approach exhibits lower code complexity and a more appropriate structure for implementing fault tolerance with respect to hardware failures.