LTL learning on GPUs

TL;DR

This paper introduces the first GPU-based LTL learning algorithm that significantly improves scalability and speed by leveraging a novel branch-free semantics with logarithmic time complexity, enabling handling of much larger trace datasets.

Contribution

It presents a novel GPU implementation of LTL learning using enumerative program synthesis and introduces a branch-free semantics with O(log n) complexity, vastly improving scalability.

Findings

Handles traces at least 2048 times larger than previous methods

Learns LTL formulas at least 46 times faster on average

Uses a novel branch-free semantics with O(log n) time complexity

Abstract

Linear temporal logic (LTL) is widely used in industrial verification. LTL formulae can be learned from traces. Scaling LTL formula learning is an open problem. We implement the first GPU-based LTL learner using a novel form of enumerative program synthesis. The learner is sound and complete. Our benchmarks indicate that it handles traces at least 2048 times more numerous, and on average at least 46 times faster than existing state-of-the-art learners. This is achieved with, among others, novel branch-free LTL semantics that has $O(\log n)$ time complexity, where $n$ is trace length, while previous implementations are $O(n^2)$ or worse (assuming bitwise boolean operations and shifts by powers of 2 have unit costs -- a realistic assumption on modern processors).