A performance study of random neural network as supervised learning tool using CUDA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099060" target="_blank" >RIV/61989100:27240/16:86099060 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/16:86099060

Výsledek na webu

<a href="http://dx.doi.org/10.6138/JIT.2016.17.4.20141014d" target="_blank" >http://dx.doi.org/10.6138/JIT.2016.17.4.20141014d</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.6138/JIT.2016.17.4.20141014d" target="_blank" >10.6138/JIT.2016.17.4.20141014d</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A performance study of random neural network as supervised learning tool using CUDA

Popis výsledku v původním jazyce

The Graphics Processing Units (GPUs) have been used for accelerating graphic calculations as well as for developing more general devices. One of the most used parallel platforms is the Compute Unified Device Architecture (CUDA), which allows implementing in parallel multiple GPUs obtaining a high computational performance. Over the last years, CUDA has been used for the implementation of several parallel distributed systems. At the end of the 80s, it was introduced a type of Neural Networks (NNs) inspired of the behavior of queueing networks named Random Neural Networks (RNN). The method has been successfully used in the Machine Learning community for solving many learning benchmark problems. In this paper, we implement in CUDA the gradient descent algorithm for optimizing a RNN model. We evaluate the performance of the algorithm on two real benchmark problems about energy sources. In addition, we present a comparison between the parallel implement in CUDA and the traditional implementation in C programming language. (C) 2016, Taiwan Academic Network Management Committee. All rights reserved.

Název v anglickém jazyce

A performance study of random neural network as supervised learning tool using CUDA

Popis výsledku anglicky

The Graphics Processing Units (GPUs) have been used for accelerating graphic calculations as well as for developing more general devices. One of the most used parallel platforms is the Compute Unified Device Architecture (CUDA), which allows implementing in parallel multiple GPUs obtaining a high computational performance. Over the last years, CUDA has been used for the implementation of several parallel distributed systems. At the end of the 80s, it was introduced a type of Neural Networks (NNs) inspired of the behavior of queueing networks named Random Neural Networks (RNN). The method has been successfully used in the Machine Learning community for solving many learning benchmark problems. In this paper, we implement in CUDA the gradient descent algorithm for optimizing a RNN model. We evaluate the performance of the algorithm on two real benchmark problems about energy sources. In addition, we present a comparison between the parallel implement in CUDA and the traditional implementation in C programming language. (C) 2016, Taiwan Academic Network Management Committee. All rights reserved.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Internet Technology

ISSN

1607-9264

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

TW - Čínská republika (Tchaj-wan)

Počet stran výsledku

8

Strana od-do

771-778

Kód UT WoS článku

000386063100017

EID výsledku v databázi Scopus

2-s2.0-84989354504