Structural Mapping of Global Optimization Algorithms to Graphics Processing Unit Architecture

Abstract

Graphics processing units (GPU) deliver a high execution efficiency for modern metaheuristic algorithms with a high computation complexity. It is crucial to have an optimal task mapping of the optimization algorithm to the parallel system architecture which strongly affects the efficiency of the optimization process. The paper proposes a novel task mapping algorithm of the parallel metaheuristic algorithm to the GPU architecture, describes problem statement for the mapping of algorithm graph model to the GPU model, and gives a formal definition of graph mapping and mapping restrictions. The algorithm graph model is a hierarchical graph model consisting of island parallel model and metaheuristic optimization algorithm model. A set of feasible mappings using mapping restrictions makes it possible to formalize GPU architecture and parallel model features. The structural mapping algorithm is based on cooperative solving of the optimization problem and the discrete optimization problem of the structural model mapping. The study outlines the parallel efficiency criteria which can be evaluated both experimentally and analytically to predict a model efficiency. The experimental section introduces the parallel optimization algorithm based on the proposed structural mapping algorithm. Experimental results for parallel efficiency comparison between parallel and sequential algorithms are presented and discussed