An Intelligent Framework for Oversubscription Management in CPU-GPU Unified Memory
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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.
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