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GPU-Based Acceleration of Quantum-Inspired Evolutionary Algorithm  

Ryoo, Ji-Hyun (Department of Electrical Engineering and Computer Science, Seoul National University)
Park, Han-Min (Department of Electrical Engineering and Computer Science, Seoul National University)
Choi, Ki-Young (Department of Electrical Engineering and Computer Science, Seoul National University)
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Abstract
Quantum-Inspired Evolutionary Algorithm(QEA) contains sufficient data-level parallelism to be naturally accelerated on GPUs. For an efficient reduction of execution time, however, careful task-mapping should be done to properly reflect the characteristics of CPU and GPU. Furthermore, when deciding which part of the application should run on GPU, we need to consider the data transfer between CPU and GPU memory spaces as well as the data-level parallelism. In addition, the usage of zero-copy host memory, proper choice of the execution configuration, and thread organization considering memory coalescing is important to further reduce the execution time. With all these techniques, we could run QEA 3.69 times faster on average in comparison with the multi-threading CPU for the case of 0-1 knapsack problem with 30,000 items.
Keywords
QEA; GPU; CUDA; Task-mapping; Memory-coalescing;
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