Data from: Efficient detection of repeating sites to accelerate phylogenetic likelihood calculations

The phylogenetic likelihood function is the major computational bottleneck
in several applications of evolutionary biology such as phylogenetic
inference, species delimitation, model selection and divergence times
estimation. Given the alignment, a tree and the evolutionary model
parameters, the likelihood function computes the conditional likelihood
vectors for every node of the tree. Vector entries for which all input
data are identical result in redundant likelihood operations which, in
turn, yield identical conditional values. Such operations can be omitted
for improving run-time and, using appropriate data structures, reducing
memory usage. We present a fast, novel method for identifying and omitting
such redundant operations in phylogenetic likelihood calculations, and
assess the performance improvement and memory savings attained by our
method. Using empirical and simulated data sets, we show that a prototype
implementation of our method yields up to 12-fold speedups and uses up to
78% less memory than one of the fastest and most highly tuned
implementations of the phylogenetic likelihood function currently
available. Our method is generic and can seamlessly be integrated into any
phylogenetic likelihood implementation.