APEX: A High-Performance Learned Index on Persistent Memory Article Swipe

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Baotong Lu , Jialin Ding , Eric Lo , Umar Farooq Minhas , Tianzheng Wang ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2105.00683

The recently released persistent memory (PM) offers high performance, persistence, and is cheaper than DRAM. This opens up new possibilities for indexes that operate and persist data directly on the memory bus. Recent learned indexes exploit data distribution and have shown great potential for some workloads. However, none support persistence or instant recovery, and existing PM-based indexes typically evolve B+-trees without considering learned indexes. This paper proposes APEX, a new PM-optimized learned index that offers high performance, persistence, concurrency, and instant recovery. APEX is based on ALEX, a state-of-the-art updatable learned index, to combine and adapt the best of past PM optimizations and learned indexes, allowing it to reduce PM accesses while still exploiting machine learning. Our evaluation on Intel DCPMM shows that APEX can perform up to ~15x better than existing PM indexes and can recover from failures in ~42ms.

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Computer Science

Parallel Computing

Artificial Intelligence

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Computer science Dram Search engine indexing Apex (geometry) Index (typography) Popularity Parallel computing Artificial intelligence Computer hardware World Wide Web Mathematics Social psychology Psychology Geometry

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2105.00683
PDF: https://arxiv.org/pdf/2105.00683
OA Status: green
Cited By: 6
References: 47
Related Works: 20
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DOI: https://doi.org/10.48550/arxiv.2105.00683

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Title: APEX: A High-Performance Learned Index on Persistent Memory

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-05-03

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Authors: Baotong Lu, Jialin Ding, Eric Lo, Umar Farooq Minhas, Tianzheng Wang

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Landing page: https://arxiv.org/abs/2105.00683

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Concepts: Computer science, Dram, Search engine indexing, Apex (geometry), Index (typography), Popularity, Parallel computing, Artificial intelligence, Computer hardware, World Wide Web, Mathematics, Social psychology, Psychology, Geometry

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Cited by: 6

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