ABSTRACT
This paper discusses the implementation of parallelism on the A* algorithm, using both the central processing unit and the graphics processing unit, in order to increase its efficiency in terms of the necessary time to solve Sliding Puzzle problems. The purpose of this paper, is to investigate the capabilities of the graphics card programming and to examine potential improvements resulting from its integration in the A* algorithm. As part of the work, data is collected from experimental tests, which are used to support hypotheses and ultimately draw conclusions and recommendations that may lead to increased efficiency by reducing the time required to solve N-Puzzle problems.
- [1] Irani, K. B. and Shih, Y. (1986). Parallel A* and AO* Algorithms: An Optimality Criterion and Performance Evaluation.. ICPP (p./pp. 274-277), : IEEE Computer Society Press.Google Scholar
- [2] Fukunaga, Alex and Botea, Adi and Jinnai, Yuu and Kishimoto, Akihiro. (2017). A Survey of Parallel A*.Google Scholar
- [3] Akihiro Kishimoto, Alex Fukunaga, Adi Botea, Evaluation of a simple, scalable, parallel best-first search strategy, Artificial Intelligence, 2013Google Scholar
- [4] Parallel N-Puzzle Solver in Haskell Zhonglin Yang (zy2496), Yuxuan Luo(yl4524) 2021/12/22Google Scholar
- [5] Edelkamp, Stefan, and Stefan Schrodl. Heuristic search: theory and applications. Elsevier, 2011.Google ScholarDigital Library
- [6] Zhou, Yichao, and Jianyang Zeng. "Massively parallel A* search on a GPU." Proceedings of the AAAI Conference on Artificial Intelligence, 2015.Google Scholar
- [7] https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.htmlGoogle Scholar
Index Terms
- CPU and GPU Parallelism of the A* Algorithm on solving N-Puzzle problems
Recommendations
Efficient CPU-GPU cooperative computing for solving the subset-sum problem
Heterogeneous CPU-GPU system is a powerful way to accelerate compute-intensive applications, such as the subset-sum problem. Many parallel algorithms for solving the problem have been implemented on graphics processing units GPUs. However, these GPU ...
Accelerating genetic algorithms with GPU computing: A selective overview
Highlights- Comprehensive survey on accelerating GAs with GPU computing.
- Major difference ...
AbstractThe emergence of GPU-CPU heterogeneous architectures has led to a fundamental paradigm shift in parallel programming. Accelerating Genetic Algorithms (GAs) on these architectures has received significant attention from both ...
GPU-parallel subtree interpreter for genetic programming
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary ComputationGenetic Programming (GP) is a computationally intensive technique but its nature is embarrassingly parallel. Graphic Processing Units (GPUs) are many-core architectures which have been widely employed to speed up the evaluation of GP. In recent years, ...
Comments