在這篇論文中，我們介紹了兩個在堆疊快取（Stack Cache）大小固定的爪哇處理器（Java Processor）上針對堆疊框架（Stack Frame）配置的 Bytecode 最佳化技術。我們實驗平台的硬體架構基本上是一顆堆疊快取大小固定的爪哇處理器，而且這顆處理器必須直接在堆疊快取上實作 JVM 的堆疊框架配置機制，以及提供堆疊搬進（Fill）與搬出（Spill）的機制。爪哇處理器具有快速運算的堆疊指令機制來加快程式執行速度，然而因為成本與處理器面積的關係，硬體的堆疊快取都有大小的限制。在執行爪哇程式的時候，會在記憶體中配置一個共用區塊來作為壘堆（Heap），此壘堆區塊在執行爪哇程式時是所有執行程式一起共用的，而且主要用來儲存爪哇程式中的實體物件（Instance Object）及矩陣（Array）。而當程式執行到某一個函式（Method）的時候，處理器會在硬體堆疊快取上配置適當連續的空間來給該函式使用，在堆疊上的這段連續空間我們稱之為堆疊框架（Stack Frame）。堆疊框架包含該函式的參數（Argument）與區域變數（Local Variable）、框架狀態（Frame State）以及運算用堆疊區域（Operand Stack）。而當函式所需求的堆疊框架如果大於處理器可以在堆疊快取上配置的大小時，則爪哇處理器將另外付出效能損失（Performance Penalty）來解決這個問題。
首先，我們提出反轉成物件（Reverse Object-unfolding）技術來解決在爪哇處理器中單一行程（Intra-procedural）呼叫的堆疊框架配置問題。針對單一行程呼叫的配置問題，我們的解決方案注重在堆疊框架中區域變數的配置。將原本配置在堆疊框架中的區域變數轉變成配置在壘堆記憶體中的物件變數，減少區域變數的個數，相對地減少堆疊框架的大小，使得處理器可以在堆疊快取上配置出適當的空間。接著，我們延伸這種堆疊配置架構來處理跨行程（Inter-procedural）呼叫的堆疊框架配置問題，並且提出一個演算法（Heuristic Algorithm）來解決跨行程呼叫的堆疊配置問題。我們的解決方式注重在減少記憶體存取以及堆疊般進（Fill）與搬出（Spill）的次數。最後，我們利用工研院（ITRI）的爪哇處理器與 Kaffe VM 模擬器（Simulator）來進行實驗，並且提出有關單一行程呼叫與跨行程呼叫的實驗數據。由實驗數據可以看出，我們所提出的兩種方案可以有效地解決在爪哇處理器上的堆疊框架配置問題。

In this thesis, we describe two techniques to optimize hardware stack machine performances on Java environments. Our hardware model basically is a Java processor with a fixed-size stack cache, which directly implements the method frame activation allocations of a software Java virtual machine (JVM). In the first category, we present a technique to solve the problem about the stack allocations for intra-procedural methods in the Java processor. We put emphasis on the issue with the local variable allocation of the method frame. A structure or object unfolding technique can be used to transform heap accesses into stack accesses. The composite object in Java is accessed via heap in memory, while object unfolding transforms heap access to scalar and stack references. For Java processors with a fixed-size stack cache, unlimited employment of structure unfolding techniques however will result in the size of local variables excelling the size of the stack cache. Thus it will reduce the performance gains. For example, ITRI-made Java processor experiences performance penalty in this scenario. To solve this problem, we propose a mechanism, reverse object-unfolding, to report an allocation scheme for a given size of the stack allocation according to our cost model. In the second category of this problem, we also extend our framework for stack allocations to deal with inter-procedural cases. We model this problem into equations and propose a heuristic algorithm based on the domain decomposition of the call graphs of a program to solve the stack allocation problems for inter-procedural cases. Our solution deals with the reduction of both memory references and stack flushes.
Our experiment is performed on the ITRI-made Java processor architecture and Kaffe VM simulator. The ITRI-made Java processor is with a fixed-size stack cache and directly allocates the method frames on the stack cache. We report experimental results and profiling data for both intra-procedural and inter-procedural cases. The experiments indicate our proposed methods are promising in speedup Java programs on the Java processor with a fixed-size stack cache.

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