| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 101 === Due to the demand for realtime, high-defination 3D graphics in video
game, graphic processing unit (GPU) has advanced to have tremendous computational power in the past two decades. Since NVIDIA released the compute unified device architecture (CUDA), GPU has become a general parallel computing device that facilitates heterogeneous computing between CPU
and GPU. GPU has enabled lots of scalable parallel programs in a wide range
of fields and parallelization is a straightforward approach to enhance the efficiency for evolutionary computation due to its inherently parallel nature.
However, parallelization of model building for EDA on GPU is rarely studied.
In this thesis, we propose two implementations on CUDA to speed up the
model building in the extended compact genetic algorithm (ECGA). The first
implementation is algorithmically identical to original ECGA. Aiming at a
greater speed boost, the second implementation modifies the model building.
It slightly decreases the accuracy of models in exchange for more speedup.
Empirically, the first implementation achieves a speedup of roughly 374 to
the baseline on 550-bit trap problem with order 5, and the second implementation achieves a speedup of roughly 531 to the baseline on the same problem.
Finally, both of our implementations scale up to 9,800-bit trap problem with
order 5 on one single Tesla C2050 GPU card.
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