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arXiv · 2412.12579

Scaling Inter-procedural Dataflow Analysis on the Cloud

Zewen Sun·Yujin Zhang·Duanchen Xu·Yiyu Zhang·Yun Qi·Yueyang Wang·Yi Li·Zhaokang Wang·Yue Li·Xuandong Li·Zhiqiang Zuo·Qingda Lu·Wenwen Peng·Shengjian Guo
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Abstract

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, 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 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 millions lines of code in minutes. Compared with the state-of-the-art, BigDataflow achieves much more analysis efficiency.

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Zewen Sun, Yujin Zhang, Duanchen Xu, Yiyu Zhang, Yun Qi, Yueyang Wang, Yi Li, Zhaokang Wang, Yue Li, Xuandong Li, Zhiqiang Zuo, Qingda Lu, Wenwen Peng, Shengjian Guo. 2024-12-17. Scaling Inter-procedural Dataflow Analysis on the Cloud. https://arxiv.org/abs/2412.12579

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