LTL learning on GPUs
This addresses a bottleneck in industrial verification by enabling faster and larger-scale LTL learning, though it is incremental as it builds on existing methods with hardware acceleration.
The paper tackles the problem of scaling Linear Temporal Logic (LTL) formula learning from traces by implementing the first GPU-based learner, achieving at least 2048 times more numerous traces and on average 46 times faster performance than existing state-of-the-art methods.
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).