Non-linear prediction of rendering workload for grid infrastructure

Doulamis, N; Doulamis, A

dc.contributor.author	Doulamis, N	en
dc.contributor.author	Doulamis, A	en
dc.date.accessioned	2014-03-01T01:54:29Z
dc.date.available	2014-03-01T01:54:29Z
dc.date.issued	2005	en
dc.identifier.issn	12300535	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27405
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-14644411728&partnerID=40&md5=52fc59af40b88ba00a4a08dac29ef1fc	en
dc.subject	3D rendering	en
dc.subject	Grid computing	en
dc.subject	Neural networks	en
dc.subject.other	Algorithms	en
dc.subject.other	Animation	en
dc.subject.other	Computational methods	en
dc.subject.other	Image processing	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Neural networks	en
dc.subject.other	Personal computers	en
dc.subject.other	Personal digital assistants	en
dc.subject.other	Problem solving	en
dc.subject.other	Supercomputers	en
dc.subject.other	3D rendering	en
dc.subject.other	Biological systems	en
dc.subject.other	Grid computing	en
dc.subject.other	Irradiance analysis	en
dc.subject.other	Nonlinear systems	en
dc.title	Non-linear prediction of rendering workload for grid infrastructure	en
heal.type	journalArticle	en
heal.publicationDate	2005	en
heal.abstract	Grid computing clusters a wide variety of geographically distributed resources. As a result it can be considered as a promising platform for solving large scale intensive problems. For this reason, it can be viewed as one of the hottest issues in the computer society. A computational intensive application which can be gained from such a Grid infrastructure, is rendering, a process dealing with creating realistic computer-generated image and with many applications ranging from simulation to design and entertainment. To implement, however, a rendering process in a Grid infrastructure prediction of its computational complexity is required. In this paper, this is addressed by using several neural network modules, each of which is appropriate for a given rendering process. For this reason, a feature vector is constructed initially, to describe with high efficiency the parameters affecting the complexity of a rendering algorithm. The feature vector is estimated by parsing a file in a RIB format. Then, prediction is performed using a neural network model. Predictions for three types of rendering algorithms are examined; the ray tracing, the radiosity and the Monte Carlo irradiance analysis.	en
heal.journalName	Machine Graphics and Vision	en
dc.identifier.volume	13	en
dc.identifier.issue	1-2	en
dc.identifier.spage	123	en
dc.identifier.epage	135	en