  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).
