Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
Proceedings of the 2nd workshop on Bio-inspired algorithms for distributed systems
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
Many-threaded implementation of differential evolution for the CUDA platform
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Differential evolution based human body pose estimation from point clouds
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Modern GPUs enable widely affordable personal computers to carry out massively parallel computation tasks. NVIDIA's CUDA technology provides a wieldy parallel computing platform. Many state-of-the-art algorithms arising from different fields have been redesigned based on CUDA to achieve computational speedup. Differential evolution (DE), as a very promising evolutionary algorithm, is highly suitable for parallelization owing to its data-parallel algorithmic structure. However, most existing CUDA-based DE implementations suffer from excessive low-throughput memory access and less efficient device utilization. This work presents an improved CUDA-based DE to optimize memory and device utilization: several logically-related kernels are combined into one composite kernel to reduce global memory access; kernel execution configuration parameters are automatically determined to maximize device occupancy; streams are employed to enable concurrent kernel execution to maximize device utilization. Experimental results on several numerical problems demonstrate superior computational time efficiency of the proposed method over two recent CUDA-based DE and the sequential DE across varying problem dimensions and algorithmic population sizes.