Sun, ZewenXu, DuanchenZhang, YiyuQi, YunWang, YueyangZuo, ZhiqiangWang, ZhaokangLi, YueLi, XuandongLu, QingdaPeng, WenwenGuo, Shengjian2024-03-012024-03-012023-11-30https://hdl.handle.net/10919/118229Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, program comprehension, etc. Despite its importance, performing interprocedural dataflow analysis on large-scale programs is well known to be challenging.In this paper, we propose a novel distributed analysis framework supporting the general interprocedural dataflow analysis.Inspired by large-scale graph processing, we devise a dedicated distributed worklist algorithm tailored for interprocedural dataflow analysis. We implement the algorithm and develop a distributed framework called BigDataflow running on a large-scale cluster.The experimental results validate the promising performance of BigDataflow – it can finish analyzing the program of millions lines of code in minutes. Compared with the state-of-the-art, BigDataflow achieves much more analysis efficiency.application/pdfenIn CopyrightArticle - Refereed2024-01-01The author(s)https://doi.org/10.1145/3611643.3616348