Abstract
Based on the analysis of the K-Nearest Neighbor Algorithm, the feasibility of parallelization is studied from the steps of the algorithm, the operation efficiency and the data structure of each step, and the part of parallel execution is determined. A K-Nearest Neighbor Algorithm parallelization scheme is designed and the parallel G-KNN algorithm is implemented in the CUDA environment. The experimental results show that the K-Nearest Neighbor Algorithm has a significant improvement in efficiency after parallelization, especially on large-scale data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
