The Multi-Processor Scheduling Problem in Phylogenetics

Abstract

Advances in wet-lab sequencing techniques allow for sequencing between 100 genomes up to 1000 full transcriptomes of species whose evolutionary relationships shall be disentangled by means of phylogenetic analyses. Likelihood-based evolutionary models allow for partitioning such broad phylogenomic datasets, for instance into gene regions, for which likelihood model parameters (except for the tree itself) can be estimated independently. Present day phylogenomic datasets are typically split up into 1000-10,000 distinct partitions. While the likelihood on such datasets needs to be computed in parallel because of the high memory requirements, it has not yet been assessed how to optimally distribute partitions and/or alignment sites to processors, in particular when the number of cores is significantly smaller than the number of partitions. We find that, by distributing partitions (of varying lengths) monolithically to processors, the induced load distribution problem essentially corresponds to the well-known multiprocessor scheduling problem. By implementing the simple Longest Processing Time (LPT) heuristics in the PThreads and MPI version of RAxML-Light, we were able to accelerate run times by up to one order of magnitude. Other heuristics for multi-processor scheduling such as improved MultiFit, improved Zero-One, or the Three Phase approach did not yield notable performance improvements.