Efficiency of parallel minimax algorithm for game tree search

Borovska, Plamenka; Lazarova, Milena

doi:10.1145/1330598.1330615

Cited by 15 publications

(12 citation statements)

References 7 publications

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“…In fact, many computer games can be solved by the above procedure similarly. Representative methods include minimax algorithm, [3] and its variant [9], and negamax algorithm with alpha-beta pruning, [20]. Here, the negamax algorithm with alpha-beta pruning is shown in Algorithm 1.…”

Section: The Problemmentioning

confidence: 99%

A Parallel Algorithm for Game Tree Search Using GPGPU

Liu

Wang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Game tree search is a classical problem in the field of game theory and artificial intelligence. Fast game tree search algorithm is critical for computer games asking for real-time responses. In this paper, we focus on how to leverage massive parallelism capabilities of GPU to accelerate the speed of game tree search algorithms and propose a concise and general parallel game tree search algorithm on GPU. The performance model of our algorithm is presented and analyzed theoretically. We implement the algorithm for two real computer games called Connect6 and Chess. We also use these two games to verify the effectiveness and efficiency of our algorithm. Experiments support our theoretical results and show good performance of our approach. Compared to classical CPU-based game tree search algorithms, our algorithm can achieve speedups of 89.95x for Connect6 and 11.43x for Chess, in case of no pruning. When pruning is considered, which means the practical performance of our algorithm, the speedup can reach about 10.58x for Connect6 and 7.26x for Chess. The insight of our work is that using GPU is a feasible way to improve the performance of game tree search algorithms.

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Section: The Problemmentioning

confidence: 99%

A Parallel Algorithm for Game Tree Search Using GPGPU

Liu

Wang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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“…The classical algorithm of game tree search is minimax [2] algorithm and its variants [6], especially negamax algorithm. For most of games such as Go [27], Chess [24] or Connect6 [32], whose game tree complexity is much more than 124 10 and at least EXPTIME-complete problems, we cannot search the game tree completely because of its exponential growth of space complexity.…”

Section: Amentioning

confidence: 99%

“…Meanwhile, for the game treeT , we have that most of the calculating nodes in game tree are leaves. From (1),(2) and(3), we can get…”

mentioning

confidence: 99%

A Node-based Parallel Game Tree Algorithm Using GPUs

Liu

et al. 2012

2012 IEEE International Conference on Cluster Computing

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Game tree search is a classical problem in the field of game theory and artificial intelligence. Fast game tree algorithm is critical for computer games asking for real-time responses. In this paper, we focus on how to leverage massive parallelism capabilities of GPUs to accelerate the speed of game tree algorithms and propose a concise and general parallel game tree algorithm on GPUs. The performance model of the algorithm is presented and analyzed theoretically. We also implement the algorithm for a real computer game called Connect6 and use it to verify the effectiveness and efficiency of our algorithm. Experiments support our theoretical results and show good performance of our approach. Compared to classical CPU-based game tree algorithms, our algorithm can achieve speedup of 70.8 in case of no pruning. When pruning is considered (which means the practical performance of our algorithm), the speedup can reach about 7.0. The insight of our work is that using GPUs is a feasible way to improve the performance of game tree algorithms.

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“…Third, comparing the modified GPU algorithm with previous parallel algorithms based on threads on CPU and iterative dependence algorithm on GPU. Fourth, analyzing the performance measures for the sequential CPU, parallel CPU using threads [5], parallel GPU using iterative dependence [6] [7] [8] and dynamic parallelism [9].…”

Section: Introductionmentioning

confidence: 99%

Enhanced parallel NegaMax tree search algorithm on GPU

Elnaggar

Gadallah

Aziem

et al. 2014

2014 IEEE International Conference on Progress in Informatics and Computing

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Parallel performance for GPUs today surpasses the traditional multi-core CPUs. Currently, many researchers started to test several AI algorithms on GPUs instead of CPUs, especially after the release of libraries such as CUDA and OpenCL that allows the implementation of general algorithms on the GPU. One of the most famous game tree search algorithms is Negamax, which tries to find the optimal next move for zero sum games. In this research, an implementation of an enhanced parallel NegaMax algorithm is presented, that runs on GPU using CUDA library. The enhanced algorithms use techniques such as no divergence, dynamic parallelism and shared GPU table. The approach was tested in checkers and chess games. It was compared with previous studies, including threads on CPU for up to 6x speedup for an 8 core processor and threads on GPU using iterative dependence and fixed grid and block size for up to 40x speedup at 14 depth. Furthermore, the approach was tested with different depths on the CPU and the GPU. The result shows speed up for parallel GPU up to 80x at 14 depth for checkers game and 90x at 14 depth for chess game, which doubled the previous research results.

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Efficiency of parallel minimax algorithm for game tree search

Cited by 15 publications

References 7 publications

A Parallel Algorithm for Game Tree Search Using GPGPU

A Parallel Algorithm for Game Tree Search Using GPGPU

A Node-based Parallel Game Tree Algorithm Using GPUs

Enhanced parallel NegaMax tree search algorithm on GPU

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