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Running “ Zen ” on Computer Clusters

Running “ Zen ” on Computer Clusters. H IDEKI K ATO † and I KUO T AKEUCHI † † The University of Tokyo November 13 th , 2009. Contents. Background Related Work Parallel Monte-Carlo Tree Search Our Architecture Experiments Conclusion and Future Work. Background. Challenge

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Running “ Zen ” on Computer Clusters

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  1. Running “Zen” on Computer Clusters HIDEKI KATO† and IKUO TAKEUCHI† † The University of Tokyo November 13th, 2009

  2. Contents Background Related Work Parallel Monte-Carlo Tree Search Our Architecture Experiments Conclusion and Future Work

  3. Background • Challenge • Can beat human professional players? • Interests • How “Zen” on an HPC cluster is strong? • Also, on a pc cluster at home? • Distributed MCTS on the Internet • Real world applications of MCTS • Provides smarter planning for intelligent robots, intelligent vehicles, etc • Environment: Many small processors on a LAN

  4. Related Work • S. Gelly et al. introduced SMT PMCTS for shared-memory SMP systems (2006) • T. Cazenave et al. proposed and evaluated three PMCTS algorithms on a 16 Intel Pentium-4 MPI cluster (2007) • G. Chaslot et al. evaluated root, leafandtreeparallelization on 2 x 8 core IBM Power5 (2008) • S. Gelly et al. proposed a combination of tree and root parallel MCTS for MPI clusters of shared-memory SMP nodes (2008) • H. Kato et al. proposed and evaluated a leaf parallel MCTS on an asymmetrical pc cluster (2008)

  5. Parallel Monte Carlo Tree Search • Tree parallelization • Symmetrical multithread parallelism on shared memory multiprocessor systems • Used by almost all MC Go programs • Leaf parallelization • For asymmetrical computer clusters • Fudo Go • Root parallelization • Shares search tree in part • Less communication • Best match with HPC clusters • MoGo, Many Faces of Go and Fuego

  6. Leaf parallelization Root parallelization Global lock Local locks Parallel Monte Carlo Tree Search (cont’d) Tree parallelization G. Chaslot, et al. 2007

  7. Our Architecture • Requirements • Since “Zen” is a commercial product, less modification is better • Root parallelization should be the best • Can run on non-MPI environments • Master manages GTP communications and conducts exchanging root information • Broadcasts incoming messages and sends answers back by the majority rule to/from the slaves • Slaves send their root information according to the master’s message • Master and slave programs are built into “Zen” for convenience and shorter delay

  8. Master Master Root information GTP Broadcast Majority rule Gather, Average, & Broadcast Slave 1 Slave 1 Slave n Slave n Our Architecture (cont’d)

  9. Experiments • No majority rule • All results are Elo ratings against Zen (self-play) on the same PC

  10. Winning rate of self-play vs. Number of node computers (9 x 9) +400 HA8000 1 thread self-play 0.3s/move Handcraft pc 1 thread self-play 0.3s/move MoGo +300 +200 Winning rate (Elo) +100 0 -100 0 1 2 3 4 5 6 Log2 Number of node computers (master and slaves) Scalability

  11. Conclusion and Future Work • We have implemented a root parallelization version of Zen by adding about 1,500 line of C++ code using Boost asio library • Not good scalability on HA8000 HPC cluster on 9 x 9 board • How about 19 x 19? How about other hardware? • Benchmark on 19 x 19

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