In-Storage Acceleration of Graph-Traversal-Based Approximate Nearest Neighbor Search
CoRR(2023)
摘要
Approximate nearest neighbor search (ANNS) is a key retrieval technique for
vector database and many data center applications, such as person
re-identification and recommendation systems. Among all the ANNS algorithms,
graph-traversal-based ANNS achieves the highest recall rate. However, as the
size of dataset increases, the graph may require hundreds of gigabytes of
memory, exceeding the main memory capacity of a single workstation node.
Although we can do partitioning and use solid-state drive (SSD) as the backing
storage, the limited SSD I/O bandwidth severely degrades the performance of the
system. To address this challenge, we present NDSearch, a near-data processing
(NDP) solution for ANNS processing. NDSearch consists of a novel in-storage
computing architecture, namely, SEARSSD, that supports the ANNS kernels and
leverages logic unit (LUN)-level parallelism inside the NAND flash chips.
NDSearch also includes a processing model that is customized for NDP and
cooperates with SEARSSD. The processing model enables us to apply a two-level
scheduling to improve the data locality and exploit the internal bandwidth in
NDSEARCH, and a speculative searching mechanism to further accelerate the ANNS
workload. Our results show that NDSearch improves the throughput by up to
31.7x, 14.6x, 7.4x, 2.9x over CPU, GPU, a state-of-the-art SmartSSD-only
design, and DeepStore, respectively. NDSEARCH also achieves two
orders-of-magnitude higher energy efficiency than CPU and GPU.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要