TY - JOUR
AU - Guo, Shengjian
AB - 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.
TI - Scaling Inter-procedural Dataflow Analysis on the Cloud
JF - Computing Research Repository
DO - 10.48550/arxiv.2412.12579
DA - 2024-12-17
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/scaling-inter-procedural-dataflow-analysis-on-the-cloud-FIszYh3KsT
VL - 2024
IS - 2412
DP - DeepDyve
ER -