Parallel search algorithm for finding the closest object in a collection of polygonal models
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Sprache: | Russisch |
| Veröffentlicht: |
Zenodo
2023
|
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866902179690840064 |
|---|---|
| author | Aksoy Tevfik Oguzhan |
| author_facet | Aksoy Tevfik Oguzhan |
| contents | <p>In this paper, we consider the problem of finding an element of a collection of polygonal models that is closest to a given object using parallel computing. Options for parallelizing the solution to this problem are proposed, with static and dynamic load balancing, which made it possible to significantly speed up the search process. The developed algorithm was implemented in the Python programming language using the MPI for Python library, which supports parallel computing, and tested on a collection of several thousand models on a local machine and on the IBM Polus high-performance computing system. The results of computational experiments have shown that the developed algorithm significantly outperforms the sequential search for the closest object in terms of computation speed. The algorithm proposed in this paper can be used in various fields, such as computer graphics, computer vision, robotics, and others.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_8064031 |
| institution | Zenodo |
| language | rus |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Parallel search algorithm for finding the closest object in a collection of polygonal models Aksoy Tevfik Oguzhan <p>In this paper, we consider the problem of finding an element of a collection of polygonal models that is closest to a given object using parallel computing. Options for parallelizing the solution to this problem are proposed, with static and dynamic load balancing, which made it possible to significantly speed up the search process. The developed algorithm was implemented in the Python programming language using the MPI for Python library, which supports parallel computing, and tested on a collection of several thousand models on a local machine and on the IBM Polus high-performance computing system. The results of computational experiments have shown that the developed algorithm significantly outperforms the sequential search for the closest object in terms of computation speed. The algorithm proposed in this paper can be used in various fields, such as computer graphics, computer vision, robotics, and others.</p> |
| title | Parallel search algorithm for finding the closest object in a collection of polygonal models |
| url | https://doi.org/10.5281/zenodo.8064031 |