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 396$\times$ over the baseline metaheuristic as well as significant energy savings with regard to other parallel approaches, without impacting multiobjective solution quality.

由于更现实的公式的定义和更大输入数据的可用性，优化问题正变得越来越困难。幸运的是，最先进的异构系统的计算能力代表了处理这些问题的主要复杂性因素的机会。这些平台为定义强大的元启发式求解器打开了大门，其中不同性质的并行计算可以有效地映射到最合适的架构和硬件资源。这项工作研究了多级并行性和异构计算的结合，以解决生物信息学中一个重要的多目标问题：系统发育学。一种并行元启发式方法，基于在算法、迭代、和解决方案级别，旨在解决 CPU + GPU 系统上的计算密集型推理。根据处理 CPU 和 GPU 之间交互的方式，还讨论了不同的异构设计替代方案。对真实世界生物数据集提案的实验评估指出了使用多层次、异构策略的好处，报告加速高达 396$\times$ 在不影响多目标解决方案质量的情况下，超过基线元启发式以及与其他并行方法相比显着节能。