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P2 Direct Memory Translation for Virtualized Clouds

面向虚拟化云的直接内存地址翻译
ASPLOS Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 22024匹配度 0/10 · 该论文研究虚拟化云环境下 x86 内存翻译与页表遍历的硬件-软件协同优化,属于体系结构与内存管理方向;与研究画像关注的静态分析、LLM/Agent 程序分析、浮点程序分析及贝叶斯程序分析均无交叉,因此匹配度极低。引用 12 inbox
Jiyuan Zhang, Weiwei Jia, Siyuan Chai, Peizhe Liu, Jongyul Kim, Tianyin Xu
crossref: 2026-09-12dblp: 2026-09-12

AI 速览

一句话:提出 DMT,一种兼容 x86 的内存翻译软硬件扩展,通过 TEA 和 guest-host 协作降低虚拟化与嵌套虚拟化的页表遍历开销。

虚拟化云中内存密集型负载受虚拟内存翻译瓶颈制约,x86 嵌套翻译最多需顺序读取 24 个页表项(PTE)。论文提出 Direct Memory Translation(DMT),一种兼容 x86 的软硬件扩展。OS 将最后一级 PTE 放在连续物理内存的 Translation Entry Areas(TEA)中,并按虚拟内存区域(VMA)建立到 TEA 内 PTE 的直接映射;因进程通常只有少量主要 VMA,映射可存入少量专用寄存器。DMT 还通过 guest-host 协作直接在物理内存分配 guest TEA,绕过中间虚拟化层。它在原生、虚拟化和嵌套虚拟化下分别只需 1、2、3 次内存引用。评估显示,虚拟化下页表遍历平均加速 1.58x,应用执行平均加速 1.20x(THP 下为 1.65x 与 1.14x)。

问题
虚拟化云中内存密集型工作负载的虚拟内存翻译是关键性能瓶颈,x86 嵌套翻译需顺序获取多达 24 个 PTE。
方法
提出 DMT:OS 在连续物理内存的 TEA 中管理最后一级 PTE,按 VMA 建立直接映射并存入专用寄存器;通过 guest-host 协作直接分配 guest TEA,绕过中间虚拟化层。
评估
在原生、虚拟化、嵌套虚拟化下分别仅需 1/2/3 次内存引用;虚拟化页表遍历平均加速 1.58x(THP 1.65x),应用执行平均加速 1.20x(THP 1.14x)。
原文摘要(Abstract)

Virtual memory translation has become a key performance bottleneck of memory-intensive workloads in virtualized cloud environments. On the x86 architecture, a nested translation needs to sequentially fetch up to 24 page table entries (PTEs). This paper presents Direct Memory Translation (DMT), a hardware-software extension for x86-based virtual memory that minimizes translation overhead while maintaining backward compatibility with x86. In DMT, the OS manages last-level PTEs in a contiguous physical memory region, termed Translation Entry Areas (TEAs). DMT establishes a direct mapping from each virtual page in a Virtual Memory Area (VMA) to the corresponding PTE in a TEA. Since processes manage memory with a handful of major VMAs, the mapping can be maintained per VMA and effectively stored in a few dedicated registers. DMT further optimizes virtualized memory translation via guest-host cooperation by directly allocating guest TEAs in physical memory, bypassing intermediate virtualization layers. DMT is inherently scalable---it takes one, two, and three memory references in native, virtualized, and nested virtualized setups. Its scalability enables hardware-assisted translation for nested virtualization. Our evaluation shows that DMT significantly speeds up page walks by an average of 1.58x (1.65x with THP) in a virtualized setup, resulting in 1.20x (1.14x with THP) speedup of application execution on average.

链接与引用

DOI 原文 · DBLP

BibTeX
@inproceedings{00030CLKX24,
  title = {Direct Memory Translation for Virtualized Clouds},
  author = {Jiyuan Zhang and Weiwei Jia and Siyuan Chai and Peizhe Liu and Jongyul Kim and Tianyin Xu},
  booktitle = {ASPLOS},
  year = {2024},
  doi = {10.1145/3620665.3640358}
}