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Transposition Driven Work Scheduling in Distributed Search

This paper discusses Transposition Driven Scheduling (TDS), which enhances distributed search in one-person games like puzzles via efficient transposition table sharing. Traditional parallel search methods encounter challenges such as high lookup latencies and expensive replicated updates. TDS addresses these issues by integrating the iterative deepening A* algorithm with a non-blocking, asynchronous communication model. The advantages of TDS include reduced duplicate searches, localized table access, and effective scaling without separate load balancing. Performance comparisons reveal TDS outperforms traditional work-stealing approaches in distributed environments.

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Transposition Driven Work Scheduling in Distributed Search

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  1. Transposition Driven Work Scheduling in Distributed Search Jonathan Schaeffer John W. Romein Aske Plaat Henri E. Bal Department of Computing Science University of Alberta Department of Computer Science vrijeUniversiteit amsterdam

  2. Transposition tables • Transposition table contains values for positions that have been searched before • Accessed 1000s times per second • Problem: how to share transposition tables efficiently on a distributed-memory system • TDS solves this problem for 1-person games (puzzles) • 2-person TDS would be more complicated

  3. Outline • Traditional parallel search + distributed transposition tables • Transposition Driven Scheduling • Performance comparison • Summarize TDS advantages • Conclusions

  4. Traditional search: the search algorithm • Parallel IDA* • Uses work-stealing • Many games have transpositions • Same position reached throughdifferent sequence of moves • A transposition table caches positionsthat have been analyzed before

  5. Traditional search: the distributed transposition table Partitioned (based on a hash function) More processors  increased table size high lookup latency (blocking reads) Replicated updates expensive (broadcast writes)

  6. Transposition Driven Scheduling • Integrates IDA* and transposition table • Send work to table (non-blocking) • Advantages: • All communication is asynchronous • Asynchronous messages canbe combined

  7. Performance • Approaches: • TDS : Transposition Driven Scheduling • WS/Part : Work Stealing + Partitioned tables • WS/Repl : Work Stealing + Replicated tables

  8. Performance • Applications: • 15-puzzle • double-blank puzzle • Rubik’s cube • 128 Pentium Pros, 1.2 Gbit/s Myrinet • Highly optimized • WS/Part uses customized network firmware [ICPP ’98]

  9. Performance (Cnt’d) 15-puzzle double-blank puzzle Rubik’s cube

  10. Performance breakdown (double blank puzzle)

  11. TDS advantages • No duplicate searches • Table access is local • Communication is non-blocking • Scales well • No separate load balancing

  12. Conclusions • TDS • scheduling algorithm for parallel search • integrates parallel IDA* with transposition table • Performance comparison • TDS scales well and outperforms work-stealing • Illustrates power of asynchronous communication • Same approach was used to solve Awari

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