Page swapping remains the dominant mechanism for extending physical memory in modern operating systems. Despite the emergence of new memory technologies, swap-based memory extension continues to be widely deployed due to its transparency and compatibility with diverse storage backends. However, current Linux swap management relies on a simple priority-based policy when multiple heterogeneous backends are available, and is oblivious to page reuse behavior. As a result, fast devices are often occupied by pages that are rarely reused, while frequently refaulted pages are placed on slow devices, leading to unnecessary performance degradation under memory pressure. We present PagePilot, a reuse-aware page offloading framework for heterogeneous swap systems. PagePilot persistently tracks refault behavior across eviction and refault cycles and uses average refault distance to guide backend selection. In addition, a background migration mechanism corrects misplacements to preserve fast-device capacity for pages with imminent reuse. We implement PagePilot in the Linux 6.3 kernel and evaluate it using representative data-serving and analytics workloads. Our results show that PagePilot improves application throughput and reduces page-fault handling latency under memory pressure, while substantially increasing the fraction of refaults served by fast backends.
Xing-Ze Liu, Jiayan Dong, Xin-Yue Liu et al.· ACM Transactions on Architec...· 0 citations
Sparse matrix kernels are fundamental to scientific computing, graph analytics, and machine learning. Their GPU performance depends strongly on the input sparsity pattern and execution strategy. For the same SpMM on the same matrix, cuSPARSE exhibits a 350x performance gap between CSR and Blocked-ELL. Our study of multiple data formats, specialized systems, and sparse compilers shows that no single implementation consistently dominates across sparsity patterns and operators. This motivates a system that can adapt its representation, execution strategy, and hardware mapping to each workload and target GPU. We present SparseDitto, an LLM-based system that constructs a GPU kernel for each matrix, operator, and target GPU. SparseDitto supports SpMV, SpMM, and SpGEMM within a unified design framework. A lightweight additive model ranks established strategies using structural features of the input matrix. An architecture-aware planner then proposes several candidate designs. Coding and verification agents implement and refine them using measurements from the target GPU. Across three sparse operators and a diverse set of matrices, SparseDitto achieves a geometric-mean speedup of 2.68x over cuSPARSE on an NVIDIA RTX PRO 6000 GPU, with a maximum of 146.61x. On an NVIDIA H200 GPU, it achieves 2.79x, with a maximum of 78.5x. Its generated SpMM kernels also accelerate full-batch GCN training by up to 3.39x.
Shiyang Li, Guan Sun, Jinwei Tang et al.· 0 citations
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