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A read-efficient and write-optimized hash table for Intel Optane DC Persistent Memory

delete2024-12-01
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PRE
AI
Z
Zexuan Li
K
Kaixin Huang *
DOI:10.1016/j.future.2024.06.028delete
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摘要

摘要

En 中文
Emerging non-volatile memory technologies are driving the next generation of storage systems and durable data structures. Among them, many hash table proposals employ NVM as the storage layer for both fast access and efficient persistence. Most of them are based on the assumption that NVM has cacheline access granularity, poor write endurance, DRAM-comparable read latency and relatively higher write latency. However, a commercial non-volatile memory product, namely Intel Optane DC Persistent Memory (Optane), has some interesting features that are different from previous assumptions, such as 256-byte OptaneLine access granularity, higher read latency than DRAM and DRAM-comparable write latency, limited read/write bandwidth, and hardwarelayer wear-leveling. Confronted with the new challenges brought by Optane, we propose Rewo-Hash, a novel read-efficient and write-optimized persistent hash table. Our incremental contributions over our previous work are summarized as follows. First, provide a more detailed technical description for cached table-inclined read mechanism and log-free atomic write mechanism. Second, we devise a consistent shadowing synchronization scheme to mask the data synchronization overhead. Third, we propose a non-blocking lightweight resizing scheme and elaborate the crash recovery mechanism. Fourth, we conduct a comprehensive analysis of the implications of Intel ceasing the production of Optane, and provide a forward-looking perspective on the future of non-volatile memory. The experimental results show that compared with state-of-the-art NVM-Optimized hash tables, Rewo-Hash gains remarkable performance improvement.
Keyword:
Non-volatile memory
Hash table
Indexing

期刊

F
Future Generation Computer Systems-The International Journal of eScience
IF:
6.1
论文数:
6.9K
被引数:
2.3W

机构

S
shanghai jiao tong university
学者数:
15.7W
论文数: 11.7W
被引数: 159
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