gpuMF: a framework for parallel hybrid metaheuristics on GPU with application to the minimisation of harmonics in multilevel inverters

Abstract

Metaheuristics are non-deterministic optimisation algorithms used to solve complex problems for which classic approaches are unsuitable or unable to generate satisfying solutions in a reasonable time. Despite their effectiveness, metaheuristics require considerable computational power. Multiple efforts have been made on the development of parallel metaheuristics on graphics processing units (GPUs). Based on a massively parallel architecture, the GPU offers remarkable computing power and can provide significant speedup. However, there currently exists no software project that unites these research initiatives into a comprehensive and reusable tool. To address this shortcoming, we developed gpuMF, a framework for parallel hybrid metaheuristics on GPUs. GPU metaheuristic framework (gpuMF) exploits the intrinsic parallelism found in metaheuristics and fully utilises the massively parallel architecture of GPUs. To demonstrate the effectiveness of our framework, we use gpuMF to minimise the harmonics of multilevel inverters while providing a speedup of 276x.