• Corpus ID: 10997907

Comparing Search Algorithms Using Sorting and Hashing on Disk and in Memory

@inproceedings{Korf2016ComparingSA,
  title={Comparing Search Algorithms Using Sorting and Hashing on Disk and in Memory},
  author={Richard E. Korf},
  booktitle={IJCAI},
  year={2016}
}
  • R. Korf
  • Published in IJCAI 9 July 2016
  • Computer Science
We compare sorting and hashing for implicit graph search using disk storage. We first describe efficient pipelined implementations of both algorithms, which reduce disk I/O. We then compare the two algorithms and find that hashing is faster, but that sorting requires less disk storage. We also compare disk-based with in-memory search, and surprisingly find that there is little or no time overhead associated with disk-based search. We present experimental results on the sliding-tile puzzles… 
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4 Citations
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Methods Citations
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Citation Type

Revisiting Immediate Duplicate Detection in External Memory Search
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Solving Large Problems with Heuristic Search: General-Purpose Parallel External-Memory Search
TLDR
This work shows that classic best-first algorithms like A* can be applied to large real-world problems by exploiting disk storage and parallel processing and, in some cases, slightly relaxing the strict best- first node expansion order.
Presented at SoCS 2016 in the Previously Published Paper Track
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References

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TLDR
This work performs delayed duplicate detection without sorting, which runs in time linear in the number of nodes in practice, and performs the first complete breadth-first searches of the 2 × 7, 3 × 5, 4 &times 4, and 2 ×× 8 sliding-tile Puzzles, verifying the radius of the 4 × 4 puzzle and determining theradius of the others.
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TLDR
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TLDR
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Using TBBFS, complete breadth-first searches of the original pancake problem with 14 and 15 pancakes, and the burned pancakes problem with 11 and 12 pancakes are performed, determining the diameter of these problem spaces for the first time.
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TLDR
A new approach to duplicate detection in graph search is developed that localizes memory references by partitioning the search graph based on an abstraction of the state space, and expanding the frontier nodes of the graph in an order that respects this partition.
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TLDR
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TLDR
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The critical resource that limits the application of best-first search is memory. We present a new class of best-first search algorithms that reduce the space complexity. The key idea is to store…
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TLDR
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TLDR
It is shown that structured duplicate detection can also be used to reduce the number of slow synchronization operations needed in parallel graph search, and several techniques for integrating parallel and external-memory graph search in an efficient way are described.
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