Exploiting the Computational Power of the Graphics Card: Optimal State Space Planning on the GPU

In this paper optimal state space planning is parallelized by exploiting the processing power of a graphics card. The two exploration steps, namely selecting the actions to be applied and generating the successors, are per-formed on a graphics processing unit. Duplicate detec-tion, however, is delayed to be executed on the central processing unit. Multiple cores are employed to bypass main memory latency. To increase processing speed for exact duplicate detection, the hash tables are lock-free. Moreover, a bucket-based representation enhances the concurrent distribution of frontier states. The planner supports cost-first exploration and is able to deal with a considerable fraction of current PDDL, including numerical state variables, complex objective functions, and goal preferences. It can maximize the net-benefit. Experimental findings show visible perfor-mance gains especially for larger benchmark problems