The ART of Sharing Points-to Analysis (Extended Abstract)
<|reference_start|>The ART of Sharing Points-to Analysis (Extended Abstract): Data-flow analyses like points-to analysis can vastly improve the precision of other analyses, and help perform powerful code optimizations. However, whole-program points-to analysis of large programs tend to be expensive - both in terms of time and memory. Consequently, many compilers (both static and JIT) and program-analysis tools tend to employ faster - but more conservative - points-to analysis to improve usability. As an alternative to such trading of precision for performance, various techniques have been proposed to perform precise yet expensive fixed-point points-to analyses ahead of time in a static analyzer, store the results, and then transmit them to independent compilation/program-analysis stages that may need them. However, an underlying concern of safety affects all such techniques - can a compiler (or program analysis tool) trust the points-to analysis results generated by another compiler/tool?   In this work, we address this issue of trust, while keeping the issues of performance efficiency in mind. We propose ART: Analysis-results Representation Template - a novel scheme to efficiently and concisely encode results of flow-sensitive, context-insensitive points-to analysis computed by a static analyzer for use in any independent system that may benefit from such a highly precise points-to analysis. Our scheme has two components: (i) a producer that can statically perform expensive points-to analysis and encode the same concisely. (ii) a consumer that, on receiving such encoded results, can regenerate the points-to analysis results encoded by the artwork if it is deemed safe. We demonstrate the usage of ART by implementing a producer (in Soot) and two consumers (in Soot and the Eclipse OpenJ9 JIT compiler). We evaluate our implementation over various benchmarks from the DaCapo and SPECjvm2008 suites.<|reference_end|>
@article{halalingaiah2024the,
    title={The ART of Sharing Points-to Analysis (Extended Abstract)},
    author={Shashin Halalingaiah, Vijay Sundaresan, Daryl Maier, and V. Krishna
  Nandivada},
    journal={arXiv preprint arXiv:2409.09062},
    year={2024},
    doi={10.1145/3689803},
    archivePrefix={arXiv},
    eprint={2409.09062},
    primaryClass={cs.PL}
}