Scaling Inter-procedural Dataflow Analysis on the Cloud

TL;DR

This paper introduces BigDataflow, a distributed framework that significantly accelerates interprocedural dataflow analysis on large-scale programs by leveraging cluster computing, enabling analysis of millions of lines of code in minutes.

Contribution

The paper presents a novel distributed analysis framework with specialized algorithms for large-scale interprocedural dataflow analysis, improving efficiency over existing methods.

Findings

BigDataflow analyzes millions of lines of code in minutes.

It outperforms state-of-the-art analysis tools in efficiency.

The framework is scalable on large clusters.

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.