A Benchmark on Multi Improvement Neighborhood Search Strategies in CPU/GPU Systems

Abstract

In combinatorial optimization problems, the neighborhood search (NS) is a fundamental component for local search based heuristics. It consists of selecting a solution from a high cardinality set of neighbor solutions, by means of operations called moves. To perform this search, NS algorithms usually adopt two main approaches: selecting the first or best improving move. The Multi Improvement (MI) strategy is a recently proposed method that consists in exploring simultaneously multiple move operations during the NS phase aiming to reach good quality solutions with shorter computational steps. This paper presents a benchmark for MI strategies in hybrid CPU/GPU systems. This technique efficiently explores the CPU processing power together with the massive parallelism achieved by modern GPUs, emerging as an efficient alternative for classic CPU neighborhood search strategies. The advantage of this approach depends heavily on finding the best tradeoff between CPU and GPU processing, as well as minimizing the memory transfers involved in the process. In the experiments, several MI configurations were tested in a hybrid CPU/GPU environment presenting better results than classical neighborhood search strategies for the Minimum Latency Problem, a hard combinatorial optimization problem.