Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (2): 73-81.doi: 10.3969/j.issn.1000-5641.2023.02.009

• Computer Science • Previous Articles     Next Articles

A memory allocation strategy for learned index based on huge pages

Jialin GUAN1, Yan ZHU1, Tingliang WU1, Yan CHEN2,*(), Jingwei ZHANG1   

  1. 1. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
    2. School of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, Guangxi 541004, China
  • Received:2021-09-09 Online:2023-03-25 Published:2023-03-23
  • Contact: Yan CHEN


In the era of big data and with the continuous expansion of data, there are significant challenges with efficient access to data. Hence, designing an efficient index structure is of great significance. ALEX (updatable adaptive learned index) is a learned index that uses a machine learning model to replace the traditional B-tree index structure. Although it offers good time and space performance, it suffers from frequent page faults. In order to solve this problem and further improve the performance of ALEX, a memory pre-allocation strategy based on huge pages is proposed, on the basis of ALEX, that can help reduce the rate of memory page faults and improve the overall performance of ALEX. In the memory allocation phase, the pre-allocation strategy is adopted, and the memory free phase adopts a delayed release strategy. Experiments on the Longitudes dataset show that this strategy offers good performance.

Key words: learned index, huge pages, data access

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