Abstract
Applying the MapReduce programming paradigm to frontier search yields simple yet efficient parallel implementations of heuristic search algorithms. We present parallel implementations of Breadth-First Frontier Search (BFFS) and Breadth-First Iterative-Deepening A* (BF-IDA*). Both scale well on high-performance systems and clusters. Using the N-puzzle as an application domain, we found that the scalability of BFFS and BF-IDA* is limited only by the performance of the I/O system. We generated the complete search space of the 15-puzzle (≈ 10 trillion states) with BFFS on 128 processors in 66 hours. Our results do not only confirm that the longest solution requires 80 moves [10], but also show how the utility of the Manhattan Distance and Linear Conflicts heuristics deteriorates in hard problems. Single random instances of the 15-puzzle can be solved in just a few seconds with our parallel BF-IDA*. Using 128 processors, the hardest 15-puzzle problem took seven seconds to solve, while hard random instances of the 24-puzzle still take more than a day of computing time.
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Reinefeld, A., Schütt, T. (2010). Out-of-Core Parallel Frontier Search with MapReduce. In: Mewhort, D.J.K., Cann, N.M., Slater, G.W., Naughton, T.J. (eds) High Performance Computing Systems and Applications. HPCS 2009. Lecture Notes in Computer Science, vol 5976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12659-8_24
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DOI: https://doi.org/10.1007/978-3-642-12659-8_24
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