dc.creator | Velentzas P., Vassilakopoulos M., Corral A. | en |
dc.date.accessioned | 2023-01-31T10:31:30Z | |
dc.date.available | 2023-01-31T10:31:30Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.1145/3415958.3433071 | |
dc.identifier.isbn | 9781450381154 | |
dc.identifier.uri | http://hdl.handle.net/11615/80563 | |
dc.description.abstract | The k Nearest-Neighbor (k-NN) query is a common spatial query that appears in several big data applications. Typically, GPU devices have much larger numbers of processing cores than CPUs and faster device memory than main memory accessed by CPUs, thus, providing higher computing power. We propose and implement a new GPU-based partitioning algorithm for the k-NN query, using the CUDA runtime API. Due to partitioning, this algorithm avoids calculating distances for the whole dataset. Using synthetic and real datasets, we present an extensive experimental performance comparison against six existing algorithms. These algorithms are based on calculating distances for the whole in-memory dataset. This comparison shows that the new algorithm excels in all the conducted experiments and outperforms these six algorithms. © 2020 ACM. | en |
dc.language.iso | en | en |
dc.source | Proceedings of the 12th International Conference on Management of Digital EcoSystems, MEDES 2020 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097914801&doi=10.1145%2f3415958.3433071&partnerID=40&md5=1ee2f1a75b721fc09a12a50fefd54d98 | |
dc.subject | Ecosystems | en |
dc.subject | Motion compensation | en |
dc.subject | Nearest neighbor search | en |
dc.subject | Program processors | en |
dc.subject | Big data applications | en |
dc.subject | GPU-based algorithms | en |
dc.subject | K nearest neighbor queries | en |
dc.subject | K-nearest neighbors | en |
dc.subject | Partitioning algorithms | en |
dc.subject | Performance comparison | en |
dc.subject | Processing core | en |
dc.subject | Spatial queries | en |
dc.subject | Graphics processing unit | en |
dc.subject | Association for Computing Machinery, Inc | en |
dc.title | A Partitioning GPU-based Algorithm for Processing the k Nearest-Neighbor Query | en |
dc.type | conferenceItem | en |