Memory-Efficient Hashed Page Tables
Memory-Efficient Hashed Page Tables
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DOI：
10.1109/hpca56546.2023.10071061
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Jovan Stojkovic;Namrata Mantri;Dimitrios Skarlatos;Tianyi Xu;J. Torrellas
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文献类型：
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作者：
Jovan Stojkovic;Namrata Mantri;Dimitrios Skarlatos;Tianyi Xu;J. Torrellas
常规的Radix-Tree Page表具有可扩展性的挑战，因为TLB的地址转换可能需要进行多个内存访问由于处理冲突和其他局限性而被避开，但最近的进步使HPT令人信服。在HPT设计中，它们需要实质性的连续记忆。此论文解决了此问题，以最大程度地减少HPT的连续内存需求，它将逻辑（L2P）表介绍了（L2P）的逻辑。此外，HPT成为不连续的物理记忆块。并且，与最新的HPTS相比，间接减少了记忆的连续性要求。 ，me-hpts：（i）将连续的内存分配需求平均减少92％，（ii）平均两种最苛刻的工作负载（连续的内存要求）将性能提高8.9％。与最先进的Radix-Tree Page表相比，从64MB降低到1MB。
Conventional radix-tree page tables have scalability challenges, as address translation following a TLB miss potentially requires multiple memory accesses in sequence. An alternative is hashed page tables (HPTs) where, conceptually, address translation needs only one memory access. Traditionally, HPTs have been shunned due to high costs of handling conflicts and other limitations. However, recent advances have made HPTs compelling. Still, a major issue in HPT designs is their requirement for substantial contiguous physical memory.This paper addresses this problem. To minimize HPTs’ contiguous memory needs, it introduces the Logical to Physical (L2P) Table and the use of Dynamically-Changing Chunk Sizes. These techniques break down the HPT into discontiguous physical-memory chunks. In addition, the paper also introduces two techniques that minimize HPTs’ total memory needs and, indirectly, reduce the memory contiguity requirements. These techniques are In-place Page Table Resizing and Per-way Resizing. We call our complete design Memory-Efficient HPTs (ME-HPTs). Compared to state-of-the-art HPTs, ME-HPTs: (i) reduce the contiguous memory allocation needs by 92% on average, and (ii) improve the performance by 8.9% on average. For the two most demanding workloads, the contiguous memory requirements decrease from 64MB to 1MB. In addition, compared to state-of-the-art radix-tree page tables, ME-HPTs achieve an average speedup of 1.23× (without huge pages) and 1.28× (with huge pages).