Accelerating Knowledge Graph Relationship Queries on GPUs

Abstract

Large graphs and networks, referred to as semantic graphs, have become essential for discovering relationships between entities, objects, or concepts in modern-day applications across various fields such as medicine, engineering, and business. Hence, identifying relationships among sets of two, or more entities represents a critical challenge in numerous analysis, search, and identification applications. This challenge corresponds to finding the Steiner Tree within a given set of en tities, a well-known NP-Hard problem. Despite extensive research and proposed solutions, the focus has primarily been theoretical, leaving a significant gap for practical applications. Consequently, there is a need for real-world and fast im plementations. In this paper, we propose a GPU-accelerated implementation of a heuristic algorithm tailored to our specific application domain. This approach aims to alleviate the complexity of the problem, particularly when dealing with large knowledge graphs. Our solution guarantees having a significant speed-up time com pared to CPU execution time and provides significantly optimized procedures to achieve such speed-up.