ARIADNE: Adaptive UVM Management for Efficient GPU Memory Oversubscription

Unified Virtual Memory (UVM) simplifies GPU programming and supports memory oversubscription, but suffers from severe performance degradation under high memory pressure due to page fault overhead and thrashing. Existing approaches such as prefetching, access counter-based migration, and dynamic Zero-copy offer limited benefits and often require hardware or compiler modifications, undermining UVM's portability and ease of deployment. We present ARIADNE, a runtime UVM management framework that preserves UVM's GPU memory abstraction while ensuring high and robust performance under memory oversubscription. ARIADNE is guided by three principles: (1) pipelined fault handling to hide migration latency, (2) Sharing Degree, a runtime metric that captures thread-level access locality without requiring hardware or compiler changes, to inform placement decisions, and (3) dynamic placement of memory regions between GPU memory and Zero-copy based on real-time access patterns. Implemented entirely within NVIDIA's UVM driver, ARIADNE requires no recompilation or hardware modifications and applies transparently to any executable or closed-source GPU UVM applications. Our experimental results show that ARIADNE delivers average speedups of$1.9 \times, 5.0 \times$, and$4.8 \times$over a state-of-the-art method at$\text{1 3 0 \%, ~} \text{1 7 5 \%}$, and 300 % oversubscription, respectively, while effectively preventing thrashing and maintaining near-linear performance scaling.