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...

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Bibliographic Details
Published in:2015 Fifth International Conference on Communication Systems and Network Technologies pp. 1355 - 1359
Main Authors: Sinha, Rashmi Sharan, Singh, Satvir, Singh, Sarabjeet, Banga, Vijay Kumar
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2015
Subjects:
Biological cells
Compute Unified Device Architecture (CUDA)
General Purpose Computing on Graphics Processing Unit (GPGPU)
Genetic Algorithm (GA)
Genetic algorithms
Graphics processing units
Instruction sets
Kernel
Sociology
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Summary: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