Bigger Buffer k-d Trees on Multi-Many-Core Systems
| Autor: | Gieseke, F., Oancea, C.E., Mahabal, A., Igel, C., Heskes, T., Senger, H. |
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| Přispěvatelé: | Senger, H. |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Learning Computer Science - Distributed Parallel and Cluster Computing Data Science Computer Science - Data Structures and Algorithms Data Structures and Algorithms (cs.DS) Distributed Parallel and Cluster Computing (cs.DC) Machine Learning (cs.LG) |
| Zdroj: | Senger, H. (ed.), High Performance Computing for Computational Science – VECPAR 2018: 13th International Conference, São Pedro, Brazil, September 17-19, 2018, Revised Selected Papers, pp. 202-214 Senger, H. (ed.), High Performance Computing for Computational Science – VECPAR 2018: 13th International Conference, São Pedro, Brazil, September 17-19, 2018, Revised Selected Papers, 202-214. Cham : Springer International Publishing STARTPAGE=202;ENDPAGE=214;ISSN=0302-9743;TITLE=Senger, H. (ed.), High Performance Computing for Computational Science – VECPAR 2018: 13th International Conference, São Pedro, Brazil, September 17-19, 2018, Revised Selected Papers |
| ISSN: | 0302-9743 |
| Popis: | A buffer k-d tree is a k-d tree variant for massively-parallel nearest neighbor search. While providing valuable speed-ups on modern many-core devices in case both a large number of reference and query points are given, buffer k-d trees are limited by the amount of points that can fit on a single device. In this work, we show how to modify the original data structure and the associated workflow to make the overall approach capable of dealing with massive data sets. We further provide a simple yet efficient way of using multiple devices given in a single workstation. The applicability of the modified framework is demonstrated in the context of astronomy, a field that is faced with huge amounts of data. |
| Databáze: | OpenAIRE |
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