  This paper proposes a novel intelligent framework for oversubscription
management in CPU-GPU UVM. We analyze the current rule-based methods of GPU
memory oversubscription with unified memory, and the current learning-based
methods for other computer architectural components. We then identify the
performance gap between the existing rule-based methods and the theoretical
upper bound. We also identify the advantages of applying machine intelligence
and the limitations of the existing learning-based methods. This paper proposes
a novel intelligent framework for oversubscription management in CPU-GPU UVM.
It consists of an access pattern classifier followed by a pattern-specific
Transformer-based model using a novel loss function aiming for reducing page
thrashing. A policy engine is designed to leverage the model's result to
perform accurate page prefetching and pre-eviction. We evaluate our intelligent
framework on a set of 11 memory-intensive benchmarks from popular benchmark
suites. Our solution outperforms the state-of-the-art (SOTA) methods for
oversubscription management, reducing the number of pages thrashed by 64.4\%
under 125\% memory oversubscription compared to the baseline, while the SOTA
method reduces the number of pages thrashed by 17.3\%. Our solution achieves an
average IPC improvement of 1.52X under 125\% memory oversubscription, and our
solution achieves an average IPC improvement of 3.66X under 150\% memory
oversubscription. Our solution outperforms the existing learning-based methods
for page address prediction, improving top-1 accuracy by 6.45\% (up to 41.2\%)
on average for a single GPGPU workload, improving top-1 accuracy by 10.2\% (up
to 30.2\%) on average for multiple concurrent GPGPU workloads.
