7 research outputs found
Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment
High-dimensional vector similarity search (HVSS) is gaining prominence as a
powerful tool for various data science and AI applications. As vector data
scales up, in-memory indexes pose a significant challenge due to the
substantial increase in main memory requirements. A potential solution involves
leveraging disk-based implementation, which stores and searches vector data on
high-performance devices like NVMe SSDs. However, implementing HVSS for data
segments proves to be intricate in vector databases where a single machine
comprises multiple segments for system scalability. In this context, each
segment operates with limited memory and disk space, necessitating a delicate
balance between accuracy, efficiency, and space cost. Existing disk-based
methods fall short as they do not holistically address all these requirements
simultaneously. In this paper, we present Starling, an I/O-efficient
disk-resident graph index framework that optimizes data layout and search
strategy within the segment. It has two primary components: (1) a data layout
incorporating an in-memory navigation graph and a reordered disk-based graph
with enhanced locality, reducing the search path length and minimizing disk
bandwidth wastage; and (2) a block search strategy designed to minimize costly
disk I/O operations during vector query execution. Through extensive
experiments, we validate the effectiveness, efficiency, and scalability of
Starling. On a data segment with 2GB memory and 10GB disk capacity, Starling
can accommodate up to 33 million vectors in 128 dimensions, offering HVSS with
over 0.9 average precision and top-10 recall rate, and latency under 1
millisecond. The results showcase Starling's superior performance, exhibiting
43.9 higher throughput with 98% lower query latency compared to
state-of-the-art methods while maintaining the same level of accuracy.Comment: This paper has been accepted by SIGMOD 202
The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey
Graph Neural Networks (GNNs) are an emerging research field. This specialized
Deep Neural Network (DNN) architecture is capable of processing graph
structured data and bridges the gap between graph processing and Deep Learning
(DL). As graphs are everywhere, GNNs can be applied to various domains
including recommendation systems, computer vision, natural language processing,
biology and chemistry. With the rapid growing size of real world graphs, the
need for efficient and scalable GNN training solutions has come. Consequently,
many works proposing GNN systems have emerged throughout the past few years.
However, there is an acute lack of overview, categorization and comparison of
such systems. We aim to fill this gap by summarizing and categorizing important
methods and techniques for large-scale GNN solutions. In addition, we establish
connections between GNN systems, graph processing systems and DL systems.Comment: Accepted at ACM Computing Survey