Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Scalable parallel formulations of depth-first search
Parallel algorithms for machine intelligence and vision
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
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
Analyzing the performance of pattern database heuristics
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Phoenix rebirth: Scalable MapReduce on a large-scale shared-memory system
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
A parallel implementation of iterative-deepening-A
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
STXXL: standard template library for XXL data sets
ESA'05 Proceedings of the 13th annual European conference on Algorithms
Out-of-core parallel frontier search with mapreduce
HPCS'09 Proceedings of the 23rd international conference on High Performance Computing Systems and Applications
Solving the 24 puzzle with instance dependent pattern databases
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
MR-search: massively parallel heuristic search
Concurrency and Computation: Practice & Experience
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Pattern databases (PDBs) store heuristic estimates that are used to improve the performance of heuristic search algorithms. They are key to the success of heuristic search in many application domains. While it is known [12] that the effectiveness of PDBs critically depends on their size, current implementations use only small PDBs because they require random access to main memory. We present two MapReduce implementations that do not require random memory access and therefore enable larger PDBs than were previously possible. The first one, named MR-BFFS, is a parallel breadth-first frontier search. It is used for generating arbitrarily large PDBs out-of-core. The second one, MR-IDA*, uses out-of-core PDBs to perform a breadth-first iterative-deepening A* search. Both scale perfectly on massively parallel systems and they make use of all available resources like CPUs, distributed memories, and disks. We demonstrate the performance of our algorithms and provide, as a byproduct of this research, the first complete evaluation of dual additive PDBs for the 8-puzzle. We also provide data on larger problem spaces and discuss the effectiveness of PDBs for improving the search.