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About this paper
Yitong Song, Xuanhe Zhou, Christian S. Jensen, Jianliang Xu
This tutorial abstract systematically reviews the evolution of vector search techniques, linking their design to advancements in storage architectures, from memory-resident to heterogeneous memory-SSD and emerging cloud-native multi-tiered systems. It details I/O optimization strategies for memory-SSD setups and identifies critical research opportunities for scalable, cost-efficient vector retrieval in cloud environments.
Key insights
- The evolution of vector search is intrinsically linked to advancements in storage architectures, transitioning from memory-resident to heterogeneous memory-SSD, and now to cloud-native multi-tiered systems for greater scalability.
- I/O operations are the dominant performance bottleneck in memory-SSD heterogeneous vector search, requiring specialized index designs (e.g., block-aware layouts) and query strategies (e.g., coarse-to-fine search, batched I/O) for efficiency.
- Cloud-native multi-tiered architectures (memory-SSD-object storage) provide virtually unlimited capacity and independent scaling but introduce new complexities in tier-aware index co-design, adaptive caching, and cost optimization.
Summary by alphaXiv.
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