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GPUs have recently been explored as a new general-purpose computing platform, which are suitable for the acceleration of compute-intensive EDA applications. In this paper we describe a GPU-based one- to n-detection fault simulator for both stuck-at and transition faults, which demonstrates a 20X speedup over a commercial CPU-based fault simulator. We further show new fault-simulation-based test selection applications enabled by this accelerated fault simulation. Our results demonstrate that the tests selected from the applications achieve higher fault coverages for 1-to-n detections with steeper fault coverage curves, as well as a better delay test quality, in comparison with tests deterministically generated by commercial ATPG tools.