Szczegóły publikacji
Opis bibliograficzny
Parallel implementation of neural networks with the use of GPGPU technology OpenCL / Maciej Kłyś, Magdalena SZYMCZYK, Piotr SZYMCZYK, Mirosław GAJER // Measurement, Automation, Monitoring / Stowarzyszenie Inżynierów i Techników Mechaników Polskich. Sekcja Metrologii, Polskie Stowarzyszenie Pomiarów Automatyki i Robotyki POLSPAR ; ISSN 2450-2855. — Tytuł poprz.: Pomiary, Automatyka, Kontrola ; ISSN: 0032-4140. — 2015 — vol. 61 no. 1, s. 16–20. — Bibliogr. s. 20, Abstr.
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 93625 |
|---|---|
| Data dodania do BaDAP | 2015-11-05 |
| Rok publikacji | 2015 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Measurement Automation and Monitoring |
Abstract
The article discusses possibilities of implementing a neural network in a parallel way. The issues of implementation are illustrated with the example of the non-linear neural network. Parallel implementation of earlier mentioned neural network is written with the use of OpenCL library, which is a representative of software supporting general-purpose computing on graphics processor units (GPGPU). The obtained results demonstrate that some group of algorithms can be computed faster if they are implemented in a parallel way and run on a multi-core processor (CPU) or a graphics processing unit (GPU). In case of the GPU, the implemented algorithm should be divided into many threads in order to perform computations faster than on a multi-core CPU. In general, computations on a GPU should be performed when there is a need to process a large amount of data with the use of algorithm which is very well suited to parallel implementation.
