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Quantitative dynamic-memory analysis for Java

机译:Java的定量动态内存分析

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Space- and time-predictability are hard to achieve for object-oriented languages with automated dynamic-memory management. Although there has been significant work to design APIs, such as the Real-Time Specification for Java (RTSJ), and to implement garbage collectors to enable real-time performance, quantitative space analysis is still in its infancy. This work presents the integration of a series of compile-time analysis techniques to help predicting quantitative memory usage. In particular, we focus on providing tool assistance for identifying RTSJ scoped-memory regions, their sizes, and overall memory usage. First, the tool-suite synthesizes a memory organization where regions are associated with methods. Second, it infers their sizes in parametric closed form in terms of relevant program variables. Third, it exhibits a parametric upper bound on the amount of available free memory required to execute a method. The experiments carried out with a RTSJ benchmark, a real-time aircraft collision detector, show that semiautomatic, tool-assisted generation of scoped-based code is both helpful and doable. Copyright ? 2010 John Wiley & Sons, Ltd.
机译:对于具有自动化动态内存管理的面向对象语言,很难实现空间和时间可预测性。尽管在设计API(例如Java实时规范(RTSJ))和实现垃圾收集器以实现实时性能方面进行了大量工作,但定量空间分析仍处于起步阶段。这项工作提出了一系列编译时分析技术的集成,以帮助预测定量内存使用情况。特别是,我们专注于提供工具帮助,以识别RTSJ范围内的内存区域,其大小以及整体内存使用情况。首先,工具套件综合了一个内存组织,其中区域与方法相关联。其次,它根据相关程序变量以参数封闭形式推断其大小。第三,它展示了执行方法所需的可用空闲内存量的参数上限。使用RTSJ基准(实时飞机碰撞检测器)进行的实验表明,半自动,工具辅助的基于范围的代码生成既有用又可行。版权? 2010 John Wiley&Sons,Ltd.

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