Systolic neighborhood search on graphics processing units
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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. 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. © 2013 Springer-Verlag Berlin Heidelberg.
cuda; evolutionary algorithm; optimization; parallel computing; systolic architecture; canonical modeling; compute unified device architectures; cuda; graphics processing unit; neighborhood search; parallel processing models; systolic architecture; computational complexity; computer graphics; evolutionary algorithms; optimization; parallel architectures; parallel processing systems; program processors; systolic arrays; computer graphics equipment