Hardware Acceleration for Knowledge Graph Processing: Challenges & Recent Developments
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Date
2024-11-19
Publication Type
Working Paper
ETH Bibliography
yes
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Abstract
Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in the area of the Semantic Web as well as gaining popularity in other application domains such as data mining and search engines. Simultaneously, there has been enormous progress in the development of different types of heterogeneous hardware, impacting the way KGs are processed. The aim of this paper is to provide a systematic literature review of knowledge graph hardware acceleration. For this, we present a classification of the primary areas in knowledge graph technology that harnesses different hardware units for accelerating certain knowledge graph functionalities. We then extensively describe respective works, focusing on how KG related schemes harness modern hardware accelerators. Based on our review, we identify various research gaps and future exploratory directions that are anticipated to be of significant value both for academics and industry practitioners.
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Publication status
published
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Journal / series
Volume
Pages / Article No.
2408.12173
Publisher
Cornell University
Event
Edition / version
v2
Methods
Software
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Date collected
Date created
Subject
Knowledge Graphs; Semantic Web; Hardware Architectures; Systematic Literature Review; Graph Algorithms; Heterogeneous Hardware; FPGA; GPU; ASIC; CPU
Organisational unit
03950 - Hoefler, Torsten / Hoefler, Torsten
Notes
Funding
101002047 - Productive Spatial Accelerator Programming (EC)
955513 - MAchinE Learning for Scalable meTeoROlogy and cliMate (EC)
101070141 / 22.00308 - Green responsibLe privACy preservIng dAta operaTIONs (SBFI)
955513 - MAchinE Learning for Scalable meTeoROlogy and cliMate (EC)
101070141 / 22.00308 - Green responsibLe privACy preservIng dAta operaTIONs (SBFI)