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.
