Speedup Genetic Algorithm Using C-CUDA
Genetic Algorithm (GA) is one of most popular swarm based evolutionary search algorithm that involves multiple data independent computations. Such computations can be made parallel on GPU cores using Compute Unified Design Architecture (CUDA) platform. In this paper, various operations of GA such as...
Uloženo v:
| Vydáno v: | 2015 Fifth International Conference on Communication Systems and Network Technologies s. 1355 - 1359 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.04.2015
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | Genetic Algorithm (GA) is one of most popular swarm based evolutionary search algorithm that involves multiple data independent computations. Such computations can be made parallel on GPU cores using Compute Unified Design Architecture (CUDA) platform. In this paper, various operations of GA such as fitness evaluation, selection, crossover and mutation, etc. Are implemented in parallel on GPU cores and then performance is compared with its serial implementation. The algorithm performance in serial and in parallel implementations are examined on a test bed of well-known benchmark optimization functions. The performances are analyzed with varying parameters viz. (i)population sizes, (ii) dimensional sizes, and (iii) problems of differing complexities. Results shows that the overall computational time can substantially be decreased by parallel implementation on GPU cores. The proposed implementations resulted in 1.18 to 4.15 times faster than the corresponding serial implementation on CPU. |
|---|---|
| DOI: | 10.1109/CSNT.2015.148 |