.png){kind=icon}
返回
Pivotal B plus tree for Byte-Addressable Persistent Memory
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3170916.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Over the past few years, various indexes have been redesigned for byte-addressable persistent memory. In this work, we design and implement PB+tree (Pivotal B+tree) that resolves the limitations of state-of-the-art fully persistent B+trees. First, PB+tree reduces the number of expensive shift operations by up to half by managing two sub-arrays separated by a pivot key. Second, PB+tree reads cachelines in ascending order, which makes PB+tree benefit from hardware prefetchers and run faster than state-of-the-art persistent B+trees that access cachelines in non-contiguous or descending order. Third, PB+tree employs an optimistic lock-free search algorithm to avoid repeatedly visiting the same tree node. Although the optimistic lock-free search algorithm involves a risk of visiting incorrect child nodes, PB+tree guarantees correct search results using the lazy correction algorithm using doubly linked sibling pointers. Our performance study shows that PB+tree outperforms the state-of-the-art fully persistent indexes by a large margin. A search algorithm without optimistic locking risks visiting the wrong child node, but PB+tree uses a lazy correction algorithm with doubly linked sibling pointers to ensure correct search results. Our performance studies show that PB+trees outperform state-of-the-art fully persistent indexes.
Keyword:
Tree data structures
fault tolerance
database concurrency operations
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Ionic mobility in ammonium-rubidium heptafluorozirconate (NH4)2.4Rb0.6ZrF7 by (1H, 19F) NMR data
.png){kind=icon}
PREAI
分享收藏Do triangles play tricks? Attribution of mental states to animated shapes in normal and abnormal development三角形玩把戏吗？将心理状态归因于正常和异常发育中的动画形状
PREAI
分享收藏
An examination of the relationship between personality and aggression using the general aggression and five factor models使用一般攻击和五因素模型检查人格与攻击之间的关系
PREAI
分享收藏
查看更多内容