Accelerating Large-Scale Graph-based Nearest Neighbor Search on a Computational Storage Platform
Ji-Hoon Kim, Yeo-Reum Park, Jaeyoung Do, Soo-Young Ji, and Joo-Young, Kim

TL;DR
This paper presents a computational storage platform using SmartSSD CSDs to accelerate large-scale graph-based nearest neighbor search, achieving significant speed and energy efficiency improvements over traditional CPU and GPU systems.
Contribution
It introduces a hardware-friendly modification of the HNSW algorithm and implements accelerators on SmartSSD CSDs, scaling up to four devices for enhanced performance.
Findings
75.59 queries per second throughput on SIFT1B dataset
12.83x faster than CPU-based systems
17.91x more energy efficient than GPU-based systems
Abstract
K-nearest neighbor search is one of the fundamental tasks in various applications and the hierarchical navigable small world (HNSW) has recently drawn attention in large-scale cloud services, as it easily scales up the database while offering fast search. On the other hand, a computational storage device (CSD) that combines programmable logic and storage modules on a single board becomes popular to address the data bandwidth bottleneck of modern computing systems. In this paper, we propose a computational storage platform that can accelerate a large-scale graph-based nearest neighbor search algorithm based on SmartSSD CSD. To this end, we modify the algorithm more amenable on the hardware and implement two types of accelerators using HLS- and RTL-based methodology with various optimization methods. In addition, we scale up the proposed platform to have 4 SmartSSDs and apply graph…
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