参数资源使用静态分析的通用框架
A General Framework for Static Profiling of Parametric Resource Usage
摘要：
传统的静态资源分析是在不执行程序的情况下估计程序的总资源使用量，本文提出了一种新的资源分析方法，其目的是对累积成本进行静态分析，即，对于节目的选定部分，发现在这些部分中的每一个中累积的资源使用的估计或界限。传统的资源分析是参数化的，因为结果可以是输入数据大小的函数。我们的静态分析也是参数化的，即，我们的累积成本估计也由输入数据大小参数化。我们的建议是基于成本中心的概念和一个程序转换，允许静态推断的功能，返回这些累积的成本取决于输入数据大小的界限，为每个成本中心的利益。这样的信息比传统的资源使用函数对软件开发者更有用，因为它允许识别应该被优化的程序的部分，因为它们对程序执行的总成本的影响更大。我们还报告了我们的实施所提出的技术使用CiaoPP程序分析框架，并提供了一些实验结果。本文件正在考虑被TPLP接受。
英文摘要：
Traditional static resource analyses estimate the total resource usage of a program, without executing it. In this paper we present a novel resource analysis whose aim is instead the static profiling of accumulated cost, i.e., to discover, for selected parts of the program, an estimate or bound of the resource usage accumulated in each of those parts. Traditional resource analyses are parametric in the sense that the results can be functions on input data sizes. Our static profiling is also parametric, i.e., our accumulated cost estimates are also parameterized by input data sizes. Our proposal is based on the concept of cost centers and a program transformation that allows the static inference of functions that return bounds on these accumulated costs depending on input data sizes, for each cost center of interest. Such information is much more useful to the software developer than the traditional resource usage functions, as it allows identifying the parts of a program that should be optimized, because of their greater impact on the total cost of program executions. We also report on our implementation of the proposed technique using the CiaoPP program analysis framework, and provide some experimental results. This paper is under consideration for acceptance in TPLP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
