Phylogenetic models of rate heterogeneity: a high performance computing perspective

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Abstract
Inference of phylogenetic trees using the maximum likelihood (ML) method is NP-hard. Furthermore, the computation of the likelihood function for huge trees of more than 1,000 organisms is computationally intensive due to a large amount of floating point operations and high memory consumption. Within this context, the present paper compares two competing mathematical models that account for evolutionary rate heterogeneity: the Gamma and CAT models. The intention of this paper is to show that - from a purely empirical point of view - CAT can be used instead of Gamma. The main advantage of CAT over Gamma consists in significantly lower memory consumption and faster inference times. An experimental study using RAxML has been performed on 19 real-world datasets comprising 73 up to 1,663 DNA sequences. Results show that CAT is on average 5.5 times faster than Gamma and - surprisingly enough - also yields trees with slightly superior Gamma likelihood values. The usage of the CAT model decreases the amount of average L2 and L3 cache misses by factor 8.55