研究生: |
何泳陖 |
---|---|
論文名稱: |
基於繪圖處理器之疊代層級平行演化演算法軟體框架 A software framework for iteration-level parallel evolutionary algorithm on graphics processing units |
指導教授: | 蔣宗哲 |
學位類別: |
碩士 Master |
系所名稱: |
資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2014 |
畢業學年度: | 102 |
語文別: | 中文 |
論文頁數: | 68 |
中文關鍵詞: | 演化演算法 、CUDA 、平行處理 、PEAC |
論文種類: | 學術論文 |
相關次數: | 點閱:286 下載:11 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
生活中常會遇到最佳化問題滿例如搭乘交通工具從甲地前往乙地,可選擇的交通工具組合可能有很多種,選擇考慮的因素有最快抵達或交通費最低等等。此類問題經常使用演化演算法來求解,而撰寫一個完整的演化演算法需耗費一些時間。
NVIDIA公司致力於平行環境CUDA的開發,使用者可以將C++程式碼修改成可運行CUDA程式碼。以現今的平行計算技術,使用不高的成本購入可運行CUDA的硬體,加速數十倍至數百倍是有可能的,數小時的實驗加速後可能僅需數分鐘即可完成。
本論文開發一個演化演算法的軟體框架,PEAC(Parallel Evolutionary Algorithms base on CUDA),提供演化演算法的基本設定,也提供演化演算法中常見的運算子供使用。讓使用者可以降低撰寫程式碼的時間。也不需要額外花費時間學習CUDA的程式開發。
[1] Cuda toolkit documentation, http://docs.nvidia.com/cuda/.
[2] R. Couturier, “Introduction to cuda,” Designing Scientific Applications on GPUs, p. 13, 2013.
[3] Cuda c programming guide, http://docs.nvidia.com/cuda/cuda-c-programming-guide/.
[4] T. Van Luong, N. Melab, and E.-G. Talbi, “Gpu computing for parallel local search metaheuristic algorithms,” IEEE Transactions on Computers, vol. 62, no. 1, pp. 173–185, 2013.
[5] M. Czapiński and S. Barnes, “Tabu search with two approaches to parallel flowshop evaluation on CUDA platform,” Journal of Parallel and Distributed Computing, vol. 71, no. 6, pp. 802–811, 2011.
[6] M. Pedemonte, E. Alba, and F. Luna, “Bitwise operations for gpu implementation of genetic algorithms,” in Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, ACM, 2011, pp. 439–446.
[7] H. Wang, S. Rahnamayan, and Z. Wu, “Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems,” Journal of Parallel and Distributed Computing, vol. 73, no. 1, pp. 62–73, 2013.
[8] M. L. Wong, “Parallel multi-objective evolutionary algorithms on graph-ics processing units,” in Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, ACM, 2009, pp. 2515–2522.
[9] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and eli- tist multiobjective genetic algorithm: nsga-ii,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
[10] P. Vidal and E. Alba, “A multi-gpu implementation of a cellular genetic algorithm,” in IEEE Congress on Evolutionary Computation, 2010, pp. 1–7.
[11] M. Arenas, G Romero, A. Mora, P. Castillo, and J. Merelo, “Gpu parallel computation in bioinspired algorithms: a review,” in Advances in Intelligent Modelling and Simulation, Springer, 2012, pp. 113–134.
[12] L. Mussi, F. Daolio, and S. Cagnoni, “Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture,” Information Sciences, vol. 181, no. 20, pp. 4642–4657, 2011.
[13] D. Izzo, “PyGMO and PyKEP: open source tools for massively parallel optimization in astrodynamics (the case of interplanetary trajectory optimization),” Proceedings of the Fifth International Conference on Astrodynamics Tools and Techniques, ICATT, 2012.
[14] Y. S. Nashed, R. Ugolotti, P. Mesejo, and S. Cagnoni, “libcudaoptimize: an open source library of gpu-based metaheuristics,” in Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, ACM, 2012, pp. 117–124.
[15] O. Maitre, F. Krüger, S. Querry, N. Lachiche, and P. Collet, “Easea: specification and execution of evolutionary algorithms on gpgpu,” Soft Computing, vol. 16, no. 2, pp. 261–279, 2012.
[16] N. Melab, T. Luong, K Boufaras, and E.-G. Talbi, “Towards paradiseo-mo-gpu: a framework for gpu-based local search metaheuristics,” in Advances in Computational Intelligence, Springer, 2011, pp. 401–408.
[17] Thrust::cuda toolkit documentation, http://docs.nvidia.com/cuda/thrust/index.html.
[18] E. Taillard, “Benchmarks for basic scheduling problems,” European Journal of Operational Research, vol. 64, no. 2, pp. 278 –285, 1993, Project Management anf Scheduling, issn: 0377-2217. doi: http://dx.doi.org/10.1016/0377-2217(93)90182-M. [Online]. Avail-able: http://www.sciencedirect.com/science/article/pii/037722179390182M.