Bundling linked data structures for linearizable range queries

Bundling linked data structures for linearizable range queries
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捆绑链接数据结构以进行线性化范围查询

DOI:
10.1145/3503221.3508412
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发表时间:
2022
期刊:
The 27th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP
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通讯作者:
Palmieri, Roberto
Palmieri, Roberto
中科院分区:
--
文献类型:
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作者:
Nelson-Slivon, Jacob;Hassan, Ahmed;Palmieri, Roberto

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我们提出了捆绑引用,这是一个新的构建块,为高度并发的基于锁的链接数据结构提供线性范围查询操作。绑定引用允许范围查询遍历与目标原子快照一致的数据结构的路径。我们用三种数据结构来演示我们的技术:链表、跳跃表和二叉搜索树。我们的评估表明,在混合工作负载中,我们的设计可以将最先进的技术提高1.2 -1.8倍,对于跳跃表和1.3 -3.7倍的二叉搜索树。我们还将捆绑的数据结构集成到DBx1000内存数据库中,比相同的竞争对手获得高达40%的收益。
We present bundled references, a new building block to provide linearizable range query operations for highly concurrent lock-based linked data structures. Bundled references allow range queries to traverse a path through the data structure that is consistent with the target atomic snapshot. We demonstrate our technique with three data structures: a linked list, skip list, and a binary search tree. Our evaluation reveals that in mixed workloads, our design can improve upon the state-of-the-art techniques by 1.2x-1.8x for a skip list and 1.3x-3.7x for a binary search tree. We also integrate our bundled data structure into the DBx1000 in-memory database, yielding up to 40% gain over the same competitors.
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