页面覆盖:一个增强的虚拟内存框架,支持细粒度的内存管理

V. Seshadri, Gennady Pekhimenko, Olatunji Ruwase, O. Mutlu, Phillip B. Gibbons, M. Kozuch, T. Mowry, Trishul M. Chilimbi
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引用次数: 49

摘要

最近的许多工作都提出了一些机制,展示了以精细粒度(例如,缓存线)管理内存的潜在优势。、细粒度重复数据删除和细粒度内存保护。不幸的是,现有的虚拟内存系统以更大的粒度(例如,4 KB页面)跟踪内存,从而抑制了此类技术的有效实现。简单地减小页大小会导致页表开销和TLB压力的不可接受的增加。我们提出了一个新的虚拟内存框架,它能够有效地实现各种细粒度内存管理技术。在我们的框架中,除了常规的物理页面之外,每个虚拟页面都可以映射到一个称为页面覆盖的结构。覆盖层包含来自虚拟页的缓存行子集。覆盖层中存在的缓存线从那里访问,所有其他缓存线从常规物理页面访问。我们的页面覆盖框架支持缓存行粒度的内存管理,而不会显著改变现有的虚拟内存框架或引入高昂的开销。我们展示了我们的框架可以支持七种内存管理技术的简单而有效的实现,每种技术都有各种各样的应用程序。我们定量地评估了其中两种技术的潜在好处:写时覆盖和稀疏数据结构计算。我们的评估表明,与传统的写时复制(copy-on-write)相比,将写时覆盖(overlay-on-write)应用于fork可以提高15%的性能,并将内存容量需求平均降低53%。对于稀疏数据计算,我们的框架可以在许多真实世界的稀疏矩阵上优于最先进的基于软件的稀疏表示。我们的框架是通用的、强大的、有效的,能够以低成本实现细粒度的内存管理。
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Page overlays: An enhanced virtual memory framework to enable fine-grained memory management
Many recent works propose mechanisms demonstrating the potential advantages of managing memory at a fine (e.g., cache line) granularity-e.g., fine-grained de-duplication and fine-grained memory protection. Unfortunately, existing virtual memory systems track memory at a larger granularity (e.g., 4 KB pages), inhibiting efficient implementation of such techniques. Simply reducing the page size results in an unacceptable increase in page table overhead and TLB pressure. We propose a new virtual memory framework that enables efficient implementation of a variety of fine-grained memory management techniques. In our framework, each virtual page can be mapped to a structure called a page overlay, in addition to a regular physical page. An overlay contains a subset of cache lines from the virtual page. Cache lines that are present in the overlay are accessed from there and all other cache lines are accessed from the regular physical page. Our page-overlay framework enables cache-line-granularity memory management without significantly altering the existing virtual memory framework or introducing high overheads. We show that our framework can enable simple and efficient implementations of seven memory management techniques, each of which has a wide variety of applications. We quantitatively evaluate the potential benefits of two of these techniques: overlay-on-write and sparse-data-structure computation. Our evaluations show that overlay-on-write, when applied to fork, can improve performance by 15% and reduce memory capacity requirements by 53% on average compared to traditional copy-on-write. For sparse data computation, our framework can outperform a state-of-the-art software-based sparse representation on a number of real-world sparse matrices. Our framework is general, powerful, and effective in enabling fine-grained memory management at low cost.
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