140 citations · 268 across the 8 of their papers we have counts for
8 papers
Decoupling Disaggregated Memory Optimizations from Indexing: A Compiler-Runtime Approach
Xinpeng Zhao, Zeling Long, Chaichon Wongkham +5
Disaggregated memory (DM) decouples compute and memory into independently scalable pools, connected over a slower interconnect rather than a local bus. This decoupling is exactly w…
Aker: Density-Aware Approximate Caching for Vector Search (Extended Version)
Sukjoon Oh, Minki Kang, Dohyun Kim +5
Disk-based approximate nearest neighbor search (ANNS) incurs high I/O overhead due to frequent disk accesses during index traversal. Approximate caching, which reuses the results o…
EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates
Mocheng Li, Baotong Lu, James Cheng +1
Filtering Approximate Nearest Neighbor (FANN) search is a critical and emerging task for strengthening the query capability of vector databases, supporting applications such as rec…
DEX: Scalable Range Indexing on Disaggregated Memory [Extended Version]
Baotong Lu, Kaisong Huang, Chieh-Jan Mike Liang +2
Memory disaggregation can potentially allow memory-optimized range indexes such as B+-trees to scale beyond one machine while attaining high hardware utilization and low cost. Desi…
Are Updatable Learned Indexes Ready?
Chaichon Wongkham, Baotong Lu, Chris Liu +3
Recently, numerous promising results have shown that updatable learned indexes can perform better than traditional indexes with much lower memory space consumption. But it is unkno…
APEX: A High-Performance Learned Index on Persistent Memory
Baotong Lu, Jialin Ding, Eric Lo +2
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 da…