dc.creator | Velentzas P., Vassilakopoulos M., Corral A. | en |
dc.date.accessioned | 2023-01-31T10:31:28Z | |
dc.date.available | 2023-01-31T10:31:28Z | |
dc.date.issued | 2021 | |
dc.identifier | 10.1007/978-3-030-78428-7_21 | |
dc.identifier.isbn | 9783030784270 | |
dc.identifier.issn | 03029743 | |
dc.identifier.uri | http://hdl.handle.net/11615/80562 | |
dc.description.abstract | Algorithms for answering the k Nearest-Neighbor (k-NN) query are widely used for queries in spatial databases and for distance classification of a group of query points against a reference dataset to derive the dominating feature class. GPU devices have much larger numbers of processing cores than CPUs and faster device memory than the main memory accessed by CPUs, thus, providing higher computing power for processing demanding queries like the k-NN one. However, since device and/or main memory may not be able to host an entire, rather big, reference dataset, storing this dataset in a fast secondary device, like a Solid State Disk (SSD) is, in many practical cases, a feasible solution. We propose and implement the first GPU-based algorithms for processing the k-NN query for big reference data stored on SSDs. Based on 3d synthetic big data, we experimentally compare these algorithms and highlight the most efficient algorithmic variation. © 2021, Springer Nature Switzerland AG. | en |
dc.language.iso | en | en |
dc.source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111373696&doi=10.1007%2f978-3-030-78428-7_21&partnerID=40&md5=e46dd02bdcd9a8ac600584ff04bcee14 | |
dc.subject | Classification (of information) | en |
dc.subject | Motion compensation | en |
dc.subject | Nearest neighbor search | en |
dc.subject | Program processors | en |
dc.subject | Computing power | en |
dc.subject | Distance classification | en |
dc.subject | Feasible solution | en |
dc.subject | GPU-based algorithms | en |
dc.subject | K nearest neighbor queries | en |
dc.subject | K-nearest neighbors | en |
dc.subject | Solid state disks (SSD) | en |
dc.subject | Spatial database | en |
dc.subject | Graphics processing unit | en |
dc.subject | Springer Science and Business Media Deutschland GmbH | en |
dc.title | GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Disk-Resident Data | en |
dc.type | conferenceItem | en |