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Very large pattern databases for heuristic search

Published: 21 June 2010 Publication History

Abstract

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.

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cover image ACM Conferences
HPDC '10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
June 2010
911 pages
ISBN:9781605589428
DOI:10.1145/1851476
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 June 2010

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