Same Engine, Multiple Gears: Parallelizing Fixpoint Iteration at Different Granularities (Extended Version)

TL;DR

This paper introduces a flexible parallel fixpoint iteration engine for static analysis that adapts to different task granularities, improving analysis efficiency on large real-world programs.

Contribution

It presents a parametric parallel fixpoint engine supporting multiple task granularities, with two distinct parallelization philosophies integrated into Goblint.

Findings

Both philosophies improve analysis times on large programs.

The independent approach scales better with more cores.

The engine's flexibility allows tailored parallelization strategies.

Abstract

Fixpoint iteration constitutes the algorithmic core of static analyzers. Parallelizing the fixpoint engine can significantly reduce analysis times. Previous approaches typically fix the granularity of tasks upfront, e.g., at the level of program threads or procedures - yielding an engine permanently stuck in one gear. Instead, we propose to parallelize a generic fixpoint engine in a way that is parametric in the task granularity - meaning that our engine can be run in different gears. We build on the top-down solver TD, extended with support for mixed-flow sensitivity, and realize two competing philosophies for parallelization, both building on a task pool that schedules tasks to a fixed number of workers. The nature of tasks differs between the philosophies. In the immediate approach, all tasks access a single thread-safe hash table maintaining solver state, while in the independent approach, each task has its own state and exchanges data with other tasks via a publish/subscribe data structure. We have equipped the fixpoint engine of the static analysis framework Goblint with implementations following both philosophies and report on our results for large real-world programs.