Apart 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, and program comprehension. Despite its importance, performing inter-procedural dataflow analysis on large-scale programs is well-known to be challenging. In this article, we propose a novel distributed analysis framework supporting the general inter-procedural dataflow analysis. Inspired by large-scale graph processing, we devise dedicated distributed worklist algorithms for both whole-program analysis and incremental analysis. We implement these algorithms and develop a distributed framework called BigDataflow running on a large-scale cluster. The experimental results validate the promising performance of BigDataflow—BigDataflow can finish analyzing the program of million lines of code in minutes. Compared with the state-of-the-art, BigDataflow achieves much more analysis efficiency.

@article{paperbot3961,
  title = {Scaling Inter-procedural Dataflow Analysis on the Cloud},
  author = {Zewen Sun and Yujin Zhang and Yueyang Wang and Duanchen Xu and Yiyu Zhang and Yun Qi and Zhaokang Wang and Yue Li and Xuandong Li and Qingda Lu and Wenwen Peng and Shengjian Guo and Zhiqiang Zuo},
  journal = {ACM Transactions on Programming Languages and Systems},
  volume = {48},
  number = {1},
  year = {2026},
  doi = {10.1145/3786763}
}