GPU unified memory simplifies programming by automatically migrating pages between CPUs and GPUs, but page faults trigger migrations with hundreds of microseconds to millisecond-scale latency, stalling thousands of threads. We target this bottleneck with a page prefetching framework that predicts future faults by modeling \emph{stride} (delta) transitions in addition to addresses. Across GPU workloads, we observe that while individual fault addresses may be unique, the sequence of strides between them exhibits strong regularity, including recurring multi-step and oscillatory patterns.

We design a hybrid prefetcher centered on a novel stride Markov predictor that learns transition probabilities between consecutive strides, and an address Markov predictor acts as a fallback to capture direct page-to-page locality when stride-based patterns are insufficient. Both predictors share a similar data structure and throttling and pruning strategy, minimizing additional complexity while bounding predictor state and bandwidth pollution.

We prototype our prefetcher as a non-invasive runtime layer requiring no modifications to GPU kernels or applications. Evaluation on diverse workloads shows that the hybrid predictor achieves up to 88% accuracy with modest pollution, and conservative speedups of up to 1.48$\times$. These results demonstrate that stride-aware Markov prediction is a practical and effective mechanism for mitigating unified-memory bottlenecks while preserving programming simplicity.