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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 廖世偉 | |
dc.contributor.author | Li-Jiun Huang | en |
dc.contributor.author | 黃禮俊 | zh_TW |
dc.date.accessioned | 2021-06-15T02:27:49Z | - |
dc.date.available | 2019-04-06 | |
dc.date.copyright | 2009-08-20 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-08-17 | |
dc.identifier.citation | [1] 'GCC, the GNU Compiler Collection,' http://gcc.gnu.org/.
[2] 'Intel C++ Compiler Professional Edition for Linux,' http://software.intel.com/en-us/intel-compilers/. [3] 'TI Code Composer Studio,' http://focus.ti.com/docs/toolsw/folders/print/ccstudio.html. [4] Z. Pan, and R. Eigenmann, “Rating Compiler Optimizations for Automatic Performance Tuning,” in Proceedings of the 2004 ACM/IEEE conference on Supercomputing, 2004. [5] Z. Pan, and R. Eigenmann, “Fast, automatic, procedure-level performance tuning,” in Proceedings of the 15th international conference on Parallel architectures and compilation techniques, 2006. [6] Z. Pan, and R. Eigenmann, “Fast and Effective Orchestration of Compiler Optimizations for Automatic Performance Tuning,” in Proceedings of the International Symposium on Code Generation and Optimization, 2006. [7] J. Cavazos, G. Fursin, F. Agakov et al., “Rapidly Selecting Good Compiler Optimizations using Performance Counters,” in Proceedings of the International Symposium on Code Generation and Optimization, 2007. [8] K. Hoste, and L. E. khout, “Cole: compiler optimization level exploration,” in Proceedings of the sixth annual IEEE/ACM international symposium on Code generation and optimization, 2008. [9] E. Zitzler, and L. Thiele, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257-271, 1999. [10] E. Zitzler, M. Laumanns, and L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Technical Report TIK-Report 103, Swiss Federal Institute of Technology (ETH) Zurich, 2001. [11] K. D. Cooper, P. J. Schielke, and D. Subramanian, “Optimizing for reduced code space using genetic algorithms,” in Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems, 1999. [12] 'PHP: Hypertext Preprocessor,' http://www.php.net/. [13] 'MySQL,' http://www.mysql.com/. [14] 'Python Programming Language,' http://www.python.org/. [15] 'Standard Performance Evaluation Corporation,' http://www.spec.org/. [16] 'skia,' http://code.google.com/p/skia/. [17] D. Whitley, “A genetic algorithm tutorial,” Statistics and Computing, vol. 4, no. 2, pp. 65-85, 1994. [18] M. Obitko. 'Introduction to Genetic Algorithms,' http://www.obitko.com/tutorials/genetic-algorithms/. [19] J.-P. Rennard. 'Introduction to Genetic Algorithms,' http://www.rennard.org/alife/english/gavintrgb.html. [20] L. D. Davis, and M. Mitchell, “Handbook of Genetic Algorithms,” Van Nostrand Reinhold, 1991. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43758 | - |
dc.description.abstract | 現今編譯器擁有許多編譯選項,也提供了預先設定好的最佳化選項供使用者使用。這些選項如果挑選適當,對於程式的效能會有助益,然而增進效能的選項卻可能增加目的碼大小。由於選項數量眾多,如何挑選適當的選項,有一定專門性,對於使用者而言是個不小的負擔。即便是使用編譯器預先設定好的選項,也不一定會符合使用者的需求。本文描述我們實作的一個儀表板工具,在三種市面上可見的設備上,幫助使用者測試編譯選項,並在使用者輸入所期望的限制之下,顯示出適合的編譯選項。 | zh_TW |
dc.description.abstract | Nowadays, a compiler has many options. It also provides predefined options for certain optimization, such as performance and code size. It is good for performance if we select suitable options when compiling. However, good performance may hurt code size. How to pick up suitable options is not easy for users to meet their requirements, for example, maximize performance or minimize code size. In this thesis, we implement a dashboard to provide a way for examining the results of specific compiler options. Users can find suitable compiler options which conform to their constraint. We demonstrate out tool on three devices on the market. We show that our tool can help user to find a set of compiler options best for their requirements. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T02:27:49Z (GMT). No. of bitstreams: 1 ntu-98-R96922108-1.pdf: 377312 bytes, checksum: e5875f3a949741b945ce8e9208be62d5 (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 vii 1 序論 1 1.1 概論 1 1.2 動機 1 2 背景知識與文獻探討 4 2.1 編譯器 4 2.2 編譯器選項 4 2.3 效能最佳化 5 2.4 縮減目的碼 5 2.5 相關研究 5 3 系統設計 7 3.1 概觀 7 3.2 主要架構 8 3.2.1 Web-based Interface 9 3.2.2 Decision Engine 10 3.2.3 Empirical Searcher 12 3.2.4 Targets 14 4 Empirical Search 15 4.1 窮舉搜尋 16 4.2 隨機搜尋 16 4.3 基因演算法 16 4.3.1 基因演算法流程圖 17 4.3.2 基因演算法求解過程 18 4.3.3 基因演算法的對應 18 4.3.4 三種演算法的實作與比較 20 5 個案研究 21 5.1 實驗設定 21 5.2 尋求較小的目的碼尺寸 24 5.3 尋求最短執行時間 25 5.4 尋求最短編譯時間 26 5.5 如何避免增加目的碼大小 27 6 結論與未來展望 28 7 參考文獻 29 | |
dc.language.iso | zh-TW | |
dc.title | 經驗搜尋的儀表板之設計與實做 | zh_TW |
dc.title | Design and Implementation of a Dashboard for an Empirical Searcher | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 郭大維,黃乾綱 | |
dc.subject.keyword | 編譯器,編譯選項,基因演算法,最佳化,目的碼尺寸, | zh_TW |
dc.subject.keyword | compiler,compile options,genetic algorithm,optimization,code size, | en |
dc.relation.page | 30 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-08-17 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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