Systolic neighborhood search on graphics processing units

PABLO VIDAL; ENRIQUE ALBA; FRANCISCO LUNA VALERO

SOFT COMPUTING - (Print)

SPRINGER

Lugar: Berlin; Año: 2014 vol. 18 p. 125 - 142

1472-7643

In this paper, we propose a parallel processing model based on systolic computing merged with concepts of evolutionary algorithms. The proposed model works over a Graphics Processing Unit using the structure of threads as cells that form a systolic mesh. Data passes through those cells, each one performing a simple computing operation. The systolic algorithm is implemented using NVIDIA?s Compute Unified Device Architecture (CUDA). To investigate the behavior and performance of the proposed model we test it over a NP-complete problem. The study of systolic algorithms on GPU and the different versions of the proposal show that our canonical model is a competitive solver with efficacy and presents a good scalability behavior across different instance sizes.