Paper Info
Title
Gravitational search algorithm using CUDA: a case study in high-performance metaheuristics
Abstract
Many scientific and technical problems with massive computation requirements could benefit from the graphics processing units (GPUs) using compute unified device architecture (CUDA). Gravitational search algorithm (GSA) is a population-based metaheuristic which can be effectively implemented on GPU to reduce the execution time. Nonetheless, the performance improvement depends strongly on the process used to adapt the algorithm into CUDA environment. In this paper, we discuss possible approaches to parallelize GSA on graphics hardware using CUDA. An in-depth study of the computation efficiency of parallel algorithms and capability to effectively exploit the architecture of GPU is performed. Additionally, a comparative study of parallel and sequential GSA was carried out on a set of standard benchmark optimization functions. The results show a significant speedup while maintaining results quality which re-emphasizes the utility of CUDA-based implementation for complex and computationally intensive parallel applications.
Year
DOI
Venue
2015
10.1007/s11227-014-1360-1
The Journal of Supercomputing
Keywords
Field
DocType
Gravitational search algorithm,Graphic processing units,CUDA
Population,Graphics hardware,Computer science,CUDA,Parallel algorithm,Parallel computing,Computational science,General-purpose computing on graphics processing units,CUDA Pinned memory,Speedup,Metaheuristic
Journal
Volume
Issue
ISSN
71
4
0920-8542
Citations 
PageRank 
References 
5
0.43
15
Authors
3
Name
Order
Citations
PageRank
Amirreza Zarrabi1152.26
Khairulmizam Samsudin29213.43
Ettikan K. Karuppiah350.43