Parallel Multiobjective Metaheuristics for Inferring Phylogenies on Multicore Clusters

Abstract

The development of efficient parallel algorithms based on mixed mode programming represents one of the most popular lines of research in current bioinformatics. By exploiting hardware resources at inter-node/intra-node level, we can address grand computational challenges which involve the optimization of multiple objective functions simultaneously. In this sense, the inference of evolutionary trees represents one of the most difficult NP-hard problems in the field. Tackling such a problem requires efficient parallel designs to take advantage of the characteristics of modern multicore clusters. In this paper, we aim to solve the phylogenetic inference problem by applying MPI/OpenMP schemes to two multiobjective metaheuristics: fast non-dominated sorting genetic algorithm and multiobjective firefly algorithm. In order to assess the performance achieved by these proposals under different system and problem sizes, we have conducted experiments on six real nucleotide data sets according to a statistical methodology. Our parallel and multiobjective metrics point out the relevance of combining hybrid programming and novel bioinspired designs with regard to other parallel and biological approaches from the literature.

Accelerating the phylogenetic parsimony function on heterogeneous systems

“…For example, the MPI/Pthreads parallelization of RAxML proposed by Pfeiffer and Stamatakis defines a hybrid approach designed to combine fine‐ and coarse‐grained parallelism features. On the other hand, Santander‐Jiménez and Vega‐Rodríguez applied coarse‐grained MPI/OpenMP schemes to undertake multiobjective phylogenetic analyses on multicore clusters …”

“…Regarding the CPU implementation, we need to consider that a proper exploitation of hardware resources can be attained by combining the definition of coarse-grained parallel tasks with the SIMD capabilities in current multicore CPUs. Our CPU kernel proposal (given by Algorithm 2) defines that each work item will be responsible for performing Algorithm 2), carrying out Fitch's operations afterwards to find out the ancestral state and partial parsimony score of the current node (lines [19][20][21]. Once all the children nodes have been processed, the obtained state is stored in the S thread_id array, going on with the next internal node.…”

A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

“…Evolutionary Algorithms (EAs), which work by searching the solution space of the targeted problem iteratively and in a randomized way, have shown powerful performance in solving many real-world optimization problems [5], [6], [7], [8], [9]. Unfortunately, the search-based core makes EAs ineffective and inefficient for solving large-scale optimization problems for two reasons: 1) As the number of decision variables increases, the solution space of the problem enlarges exponentially, preventing EAs exploring effectively within reasonable amount of search iterations.…”

Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies

“…If ML and MP were chosen as objective functions, computing the result of these two functions for all trees can be very time-consuming. In general, two kinds of method can be used to enhance algorithm’s time efficiency: (1) parallelizing algorithm [ 16 , 30 , 31 ]; and (2) improving algorithm to achieve fine convergence in fewer generations. Our work is focused on the second one.…”