Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Criticizing solutions to relaxed models yields powerful admissible heuristics
Information Sciences: an International Journal
A Performance Analysis of Transposition-Table-Driven Work Scheduling in Distributed Search
IEEE Transactions on Parallel and Distributed Systems
Divide-and-Conquer Frontier Search Applied to Optimal Sequence Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Searching with Pattern Databases
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Journal of the ACM (JACM)
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Linear-time disk-based implicit graph search
Journal of the ACM (JACM)
Structured duplicate detection in external-memory graph search
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Large-scale parallel breadth-first search
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Divide-and-conquer bidirectional search: first results
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
STXXL: standard template library for XXL data sets
ESA'05 Proceedings of the 13th annual European conference on Algorithms
Very large pattern databases for heuristic search
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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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.