Scaling Inter-procedural Dataflow Analysis on the Cloud

doi:10.1145/3786763

Scaling Inter-procedural Dataflow Analysis on the Cloud

2026-03-01

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DOI：10.1145/3786763

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Abstract

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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.

Keywords:

Interprocedural dataflow analysis

distributed computing

Scaling Inter-procedural Dataflow Analysis on the Cloud

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