The maximum likelihood method is considered as one of the most reliable methods for phylogenetic tree inference. However, as the number of species increases, the approach quickly loses its applicability due to explosive exponential number of trees that need to be considered. An earlier work by one of the authors demonstrated that, by decomposing the trees into fragments called splits, and calculating the individual likelihood of each (small) split and combining them would result in a very close approximation of the true maximum likelihood value, as well as achieving significant reduction in computational cost. However, the cost was still significant for a practical number of species that need to be considered. To solve this problem, we further extend the algorithm so that it could be effectively parallelized in a Grid environment using Grid middleware such as Ninf and Jojo, and also applied combinatorial optimization techniques. Combined, we achieved over 64 times speedup over our previous results in a testbed of 16 nodes, with favorable speedup characteristics.
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