Name
Abstract | ||
|---|---|---|
Many scientific and technical problems with massive computation requirements could benefit from the Graphics Processing Units (GPUs) using Compute Unified Device Architecture (CUDA) for high speed processing. Gravitational Search Algorithm (GSA) is a population-based metaheuristic algorithm that can be effectively implemented on GPU to reduce the execution time. 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 that re-emphasizes the utility of CUDA based implementation for complex and computationally intensive parallel applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/PDCAT.2014.38 | 2014 15th International Conference on Parallel and Distributed Computing, Applications and Technologies |
Keywords | Field | DocType |
Gravitational Search Algorithm,Graphic Processing Units,CUDA | Population,Graphics hardware,CUDA,Computer science,Parallel algorithm,Parallel computing,Computational science,General-purpose computing on graphics processing units,Metaheuristic,CUDA Pinned memory,Speedup | Conference |
ISSN | Citations | PageRank |
2379-5352 | 1 | 0.37 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amirreza Zarrabi | 1 | 15 | 2.26 |
| Ettikan Kandasamy Karuppiah | 2 | 2 | 2.07 |
| Yong Keh Kok | 3 | 1 | 0.37 |
| Ngo Chuan Hai | 4 | 1 | 0.37 |
| Simon See | 5 | 81 | 10.46 |