流敏感指针分析的对象版本控制

M. Barbar, Yulei Sui, Shiping Chen
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引用次数: 3

摘要

流量敏感的点对分析比流量不敏感的点对分析提供更好的精度。传统上在控制流图上执行,它会产生大量的分析开销。为了提高性能,分级流敏感分析(SFS)是在预先计算的自定义使用(价值流)图上进行的,其中指向变量集的点在自定义使用链上稀疏传播,而不是在控制流图中的控制流上传播。SFS使不同对象的点到集的传播变得稀疏(多对象稀疏性),但是,它在同一对象的点到集的指令之间存在冗余传播(单对象稀疏性)。对象的指向集经常是重复的,导致冗余的传播和存储,特别是在实际的堆密集型程序中。我们注意到,一个简单的图预标记扩展可以在预分析中识别出这种冗余。通过这种预分析,值流图中的多个节点(指令)可以共享单个内存对象的待设置点,而不是每个节点为单个对象维护自己的待设置点。我们提出了流敏感点到分析的对象版本控制,这是一种更精细的单对象稀疏技术,它在保持相同精度的同时,允许我们避免在传播和存储点到集时出现的许多冗余。我们在15个开源程序上进行的实验表明,与SFS相比,我们的方法运行速度提高了26.22倍(平均5.31倍),内存使用减少了5.46倍(平均2.11倍)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object Versioning for Flow-Sensitive Pointer Analysis
Flow-sensitive points-to analysis provides better precision than its flow-insensitive counterpart. Traditionally performed on the control-flow graph, it incurs heavy analysis overhead. For performance, staged flow-sensitive analysis (SFS) is conducted on a pre-computed def-use (value-flow) graph where points-to sets of variables are propagated across def-use chains sparsely rather than across control-flow in the control-flow graph. SFS makes the propagation of different objects' points-to sets sparse (multiple-object sparsity), however, it suffers from redundant propagation between instructions of the same object's points-to sets (single-object sparsity). The points-to set of an object is often duplicated, resulting in redundant propagation and storage, especially in real-world heap-intensive programs. We notice that a simple graph prelabelling extension can identify much of this redundancy in a pre-analysis. With this pre-analysis, multiple nodes (instructions) in the value-flow graph can share an individual memory object's points-to set rather than each node maintaining its own points-to set for that single object. We present object versioning for flow-sensitive points-to analysis, a finer single-object sparsity technique which maintains the same precision while allowing us to avoid much of the redundancy present in propagating and storing points-to sets. Our experiments conducted on 15 open-source programs, when compared with SFS, show that our approach runs up to 26.22× faster (5.31× on average), and reduces memory usage by up to 5.46× (2.11 × on average).
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