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Title: Exploiting multi-level parallel metaheuristics and heterogeneous computing to boost phylogenetics
Authors: Santander Jiménez, Sergio
Vega Rodríguez, Miguel Ángel
Sousa, Leonel
Keywords: Evolutionary computation;Bioinformatics;Multi-level parallelism;Heterogeneous computing;High performance computing;Computación heterogénea;Paralelismo multinivel;Computación evolutiva;Computación de alto rendimiento;Bioinformática
Issue Date: 2022
Publisher: Elsevier
Abstract: Optimization problems are becoming increasingly difficult challenges as a result of the definition of more realistic formulations and the availability of larger input data. Fortunately, the computing capabilities of state-of-the-art heterogeneous systems represent an opportunity to deal with the main complexity factors of these problems. These platforms open the door to the definition of robust metaheuristic solvers, in which parallel computations of different nature can be efficiently mapped to the most suitable architectures and hardware resources. This work investigates the combination of multi-level parallelism and heterogeneous computing to address an important multiobjective problem in bioinformatics: phylogenetics. A parallel metaheuristic approach, based on the joint exploitation of parallel tasks at the algorithm, iteration, and solution levels, is proposed to tackle computationally intensive inferences on CPU+GPU systems. Different heterogeneous design alternatives are also discussed, in accordance with the way the interactions between CPU and GPU are handled. The experimental evaluation of the proposal on real-world biological datasets points out the benefits of using multi-level, heterogeneous strategies, reporting accelerations up to 396x over the baseline metaheuristic as well as significant energy savings with regard to other parallel approaches, without impacting multiobjective solution quality.
DOI: 10.1016/j.future.2021.09.011
Appears in Collections:DTCYC - Artículos

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