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研究生:莫亞橋
研究生(外文):Ya-Chiao Moo
論文名稱:支援平行核心數位訊號處理器之巢狀迴圈最佳化與跨程序分析
論文名稱(外文):Loop Nested Optimizations and Interprocedural Analysis for PAC DSP
指導教授:李政崑
指導教授(外文):Jenq-Kuen Lee
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:55
中文關鍵詞:中繼表示法巢狀迴圈最佳化跨程序分析
外文關鍵詞:ORCIntermediate RepresentationLoop Nested OptimizationInterprocedural Analysis
相關次數:
  • 被引用被引用:0
  • 點閱點閱:160
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  • 收藏至我的研究室書目清單書目收藏:0
ORC (Open Research Compiler)編譯器提供了一個強大而完整的兩階段最佳化架構,可以在編譯時期對程式最佳化。
其一是低階最佳化,施行於與組合語言一一對應的CGIR (Intermediate Representation for Code Generation)
來作為中繼表示(Intermediate Representation)。另一個是程式碼階段最佳化,
實行於WHIRL (Winning Hierarchical Intermediate Representation)。
本篇論文會對ORC做一個概略的敘述,介紹編譯器結構和實際ORC的建構方式;
並描述每個階段最佳畫的工作方式,包含了中繼表示的轉換和編譯流程。

我們的研究主要在兩種WHIRL階段的最佳化上,巢狀迴圈最佳化和跨程序分析。
分別說明在ORC上巢狀迴圈最佳化和跨程序分析的理論和演算法。
另外,將這兩種最佳化移植到平行核心數位訊號處理器(PAC DSP)上。
詳細說明移植的過程和困難點。最後在現有的平台上測試最佳化的效果和對一些有趣的結果做一些討論。
To optimize computer program in compiling time, the Open Research Compiler (ORC) provides a well integrated optimization framework with two phases of optimizations. One is the machine level applied on Intermediate Representation for Code Generation (CGIR), which is a machine level of Intermediate Representation (IR) with one to one mapping instructions. The other is the source level phase applied on Winning Hierarchical Intermediate Representation Language (WHIRL). This thesis gives an overview of ORC, introduces the compiler architecture that implemented in ORC and the evolution of ORC, and illustrates how these compiler phases work including the IR flow and the compilation process.

The research is focused on two optimizations in the WHIRL level, Loop Nested Optimization (LNO) and Interprocedural Optimization (IPA). The theoretical concepts, algorithms and the way practiced in ORC are individually illustrated. Besides, LNO and IPA are ported to a new specific architecture, Parallel Architecture Core (PAC) Digital Signal Processor (DSP). The solved porting process and issues are presented in detail. The experiments of LNO and IPA are conducted on the proposed platform to show interesting results and to demonstrate performance improvements.
1 Introduction
2 ORC Background
3 Loop Nested Optimization(LNO)
4 Interprocedural Analysis and Optimization
5 Experiments
6 Conclusion
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