Adaptive Particle Swarm Optimization with Heterogeneous Multicore Parallelism and GPU Acceleration

Abstract

Much progress has recently been made in global optimization, with particular attention devoted to robust nature-inspired stochastic methods for difficult, high-dimensional problems. This paper presents a computational study of an adaptation of one such method, particle swarm optimization (PSO), which is analyzed for parallelization on readily-available heterogeneous parallel computational hardware: specifically, multicore technologies accelerated by graphics processing units (GPUs), as well as Intel Xeon Phi co-processors accelerated with vectorization. In this heterogeneous approach, computationally-intensive, task-parallel components are performed with multicore parallelism and data-parallel elements are executed via co-processing (GPUs or vectorization). A computationally intensive adaptive PSO technique is parallelized according to this schema. In experiments with two high-dimensional and complex functions, large speedups can be obtained. Thus, a heterogeneous approach mitigates the time complexity of PSO adaptations, suggesting that other time-intensive stochastic methods can also benefit from the techniques proposed here.