| Literature DB >> 35178539 |
Dejun Teng1, Akshay Nehe1, Prajeeth Emanuel1, Furqan Baig1, Jun Kong2, Fusheng Wang1.
Abstract
Contact tracing is gaining its importance in controlling the spread of COVID-19. However, the enormous volume of the frequently sampled tracing data brings major challenges for real-time processing. In this paper, we propose a GPU-based real-time contact tracing system based on spatial proximity queries with temporal constraints using location data. We provide dynamic indexing of moving objects using an adaptive partitioning schema on GPU with extremely low overhead. Our system optimizes the retrieval of contacted pairs to match both the requirements of contact tracing scenarios and GPU centered parallelism. We propose an efficient contacts evaluation mechanism to keep only the spatially and temporally valid contacts. Our experiments demonstrate that the system can achieve sub-second level response for large-scale contact tracing of tens of millions of people, with two magnitudes of performance boost over CPU based approach.Entities:
Keywords: GPU; contact tracing; moving objects
Year: 2021 PMID: 35178539 PMCID: PMC8849613 DOI: 10.1145/3474717.3483627
Source DB: PubMed Journal: Proc ACM SIGSPATIAL Int Conf Adv Inf