Parallel Minimax Tree Searching on GPU

Rocki, Kamil; Suda, Reiji

doi:10.1007/978-3-642-14390-8_47

Cited by 20 publications

(23 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each tree node processed in a parallel manner represents a different CES variant, therefore every searched subtree may have a different structure. Similar problems have been described, for example, in the article [12] which describes the results of a minimax tree searching algorithm, also implemented using the CUDA approach.…”

Section: Resultsmentioning

confidence: 97%

USING PARALLEL BRANCH-AND-BOUND ALGORITHM ON GPUs FOR OPTIMAL DESIGN OF MULTI-PRODUCT BATCH PLANTS

Borisenko

Haidl²,

Gorlatch³

2015

Vestnik

View full text Add to dashboard Cite

Abstract:In this paper, we propose a novel implementation of the branch-andbound algorithm on a system with GPUs (Graphics Processing Units) using the Compute Unified Device Architecture (CUDA) approach. The developed implementation is used for solving a real-world problem -the optimal selection of the chemical equipment for multi-product batch plants. We propose two implementations for the searching algorithm -an iterative and a recursive one -and we describe their optimizations, as well as compare them to each other. We report experimental results about the speedup of our GPU-based implementations as compared to the sequential CPU version.

show abstract

Section: Resultsmentioning

confidence: 97%

USING PARALLEL BRANCH-AND-BOUND ALGORITHM ON GPUs FOR OPTIMAL DESIGN OF MULTI-PRODUCT BATCH PLANTS

Borisenko

Haidl²,

Gorlatch³

2015

Vestnik

View full text Add to dashboard Cite

show abstract

“…3 illustrates an Multiple-Processor I. [5], [15], [16], [17], [19], [22], [24], [28], [29], [32] II. Our method…”

Section: Pvs and Epvs Algorithmsmentioning

confidence: 99%

A Parallel Algorithm for Game Tree Search Using GPGPU

Liu

Wang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

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.

show abstract

“…Without this important feature, no one can run a recursive-based algorithm on GPU. For this reason, most previous game searching tree researches implemented iterative-based algorithms rather than recursive based algorithms [7].…”

Section: General Purpose Gpu Programmingmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Parallel Minimax Tree Searching on GPU

Cited by 20 publications

References 5 publications

USING PARALLEL BRANCH-AND-BOUND ALGORITHM ON GPUs FOR OPTIMAL DESIGN OF MULTI-PRODUCT BATCH PLANTS

USING PARALLEL BRANCH-AND-BOUND ALGORITHM ON GPUs FOR OPTIMAL DESIGN OF MULTI-PRODUCT BATCH PLANTS

A Parallel Algorithm for Game Tree Search Using GPGPU

Enhanced parallel NegaMax tree search algorithm on GPU

Contact Info

Product

Resources

About