隨著軟體開發的演進，現代系統軟體的規模變得複雜，擁有動輒數百萬行程式碼已是常態。伴隨程式碼日漸龐大，對其進行全面的效能分析與優化所需要工具與技巧難度也越來越高。
因此，本論文提出並實作一套具備可擴充性的框架，整合編譯器與多個效能分析工具，替使用者收集並儲存編譯器編譯時期的靜態分析資料、效能分析工具的動態分析資料等，而使用者則只需繼承與開發特定的模組就能取得這些資料來進行分析與優化。Crystal 採用效能分析導向的優化方式(profile-guided optimization)。
為了驗證收集的分析資料是正確且有用的，我們提出一個應用範例，將效能分析工具所收集的資料重新建構成權重函式呼叫圖(Weighted Function Call Graph)，並套用圖計算中的社群分佈的演算法，將所有函式根據 Louvain 社群偵測演算法進行分群，接著再對每個群組內的函式根據 Pettis-Hansen 演算法重新擺放順序與位置，以達到更佳的效能。在實驗中，我們以 LLVM 作為目標，選取來自 LLVM 專案中的二十個程式進行編譯測試，最終我們得到執行效能 4.5% 到 12.3% 間的提升。

In recent years, system software has become larger and more complex. There are several challenges to integrate profile data collection, analysis algorithm implementation, and optimal optimization for large scale program. Therefore, we present an extensible framework called Crystal, which is designed for wrapping multiple profilers, compilers, and analyzers to gather useful data including static and dynamic function call graph, function counts, CPU cycles, cache miss rate, etc. Besides, we combine these data into a weighted function call graph. With this framework, user can simply apply analysis and optimization algorithm to program with les worry about the complexity of profile data generated by various tools.
In this paper, we focus on framework design for wrapping various output formats from different tools. Our framework provides compiler unit for translating source code to IR, optimizer unit for applying profile-guided analysis results on IR, profiler unit for gathering accurate data, analyzer unit for determining optimization strategies and datastore unit for storing archived data.
Finally, we have an application example on our framework. We propose a methodology to change code layout replacement in two steps. First, build weighted function call graph based on profiling data and we apply a community detection algorithm called Louvain method to split entire graph into multiple partitions. Second, we apply classic Pettis-Hansen algorithm in each partition to reordering function. In our evaluation, choosing LLVM as target and design 20 test cases for measuring the performance. In consequence, it increases from 4.5% to 12.4% performance improvement better than O0.

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