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This paper improves upon Job-Level (JL) computing, a general distributed computing approach. In JL computing, a client maintains the overall search tree and parcels the overall search into coarse-grained jobs, which are then each calculated by pre-existing game-playing programs. In order to support large-scale problems such as solving 7×7 killall-Go, or building opening books for 9×9 Go or Connect6,...
Recently, Wu et al. introduced a general approach based on distributed computing named Job-Level (JL) Computing. JL Computing has been successfully used to construct the opening books of game-playing programs.? In order to support large-scale computing problems such as solving 7×7 killall-Go, or building opening books for 9×9 Go or Connect6, record databases are used to store JL computing results...
Recently, Pawlewicz and Hayward successfully solved many Hex openings based on the Scalable Parallel Depth-First Proof-Number Search algorithm (SPDFPN), which was performed in a single machine with multiple threads. However, further parallelization is limited by the number of cores a single machine can possess. This paper investigates adapting this SPDFPN solver to a distributed computing environment,...
An approach called generic job-level (JL) search was proposed to solve computer game applications by dispatching jobs to remote workers for parallel processing. This paper applies JL search to alpha-beta search, and proposes a JL alpha-beta search (JL-ABS) algorithm based on a best-first search version of MTD(f). The JL-ABS algorithm is demonstrated by using it in an opening book analysis for Chinese...
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