Use of graphics processing units for automatic synthesis of programs. (August 2015)

• Record Type: Journal Article
• Authors: da Silva, Cleomar Pereira; Dias, Douglas Mota; Bentes, Cristiana; Cavalcanti Pacheco, Marco Aurélio
• Is Part Of: Computers & electrical engineering. Volume 46(2015)
• Page Start: 112
• Page End: 122
• Publication Date: 2015-08
• Subjects: Genetic programming -- GPU acceleration -- Machine code -- Quantum-inspired algorithms -- Massive parallelism; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.04.006
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: A new quantum-inspired linear genetic programming system that runs on the GPU. Allows the synthesis of solutions for large-scale real-world problems. Eliminates the overhead of copying the fitness results from the GPU to the CPU. Proposes a new selection mechanism to recognize the programs with best evaluations. Improves performance of the GP execution through exploiting the GPU environment. Abstract: Genetic programming (GP) is an evolutionary method that allows computers to solve problems automatically. However, the computational power required for the evaluation of billions of programs imposes a serious limitation on the problem size. This work focuses on accelerating GP to support the synthesis of large problems. This is done by completely exploiting the highly parallel environment of graphics processing units (GPUs). Here, we propose a new quantum-inspired linear GP approach that implements all the GP steps in the GPU and provides the following: (1) significant performance improvements in the GP steps, (2) elimination of the overhead of copying the fitness results from the GPU to the CPU, and (3) incorporation of a new selection mechanism to recognize the programs with the best evaluations. The proposed approach outperforms the previous approach for large-scale synthetic and real-world problems. Further, it provides a remarkable speedup over the CPU execution.