The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 88-406
Full Name: Vladimír Siládi
Position: Assistant Professor
Age: ON
Sex: Male
Address: Faculty of Natural Sciences, Matej Bel University, P.O.Box 217, Dept. of Computer Science, Tajovského 40, Banská Bystrica, 974 01
Country: SLOVAKIA
Tel: +421 48 446 7125
Tel prefix:
Fax: +421 48 446 7134
E-mail address: aladarus@gmail.com
Other E-mails: siladi@fpv.umb.sk
Title of the Paper: Comparison of Design and Performance of Snow Cover Computing on GPUs and Multi-core processors
Authors as they appear in the Paper: Ladislav Huraj, Vladimír Siládi, Jozef Siláči
Email addresses of all the authors: ladislav.huraj@ucm.sk, siladi@fpv.umb.sk, silaci@fpv.umb.sk
Number of paper pages: 11
Abstract: The aim of this work is the depth of the snow cover computing in the desired point based on the geographical characteristics of a specific geographical point in a modeled area. The measured data are known on few places. These places are coincident with raingauge stations. These data have been collected by many continuous observations and measurements at the specific climatologic raingauging stations of Slovak Hydrometeorogical Institute. An interpolation method is necessary to obtain a representation of real situation about whole surface. The main characteristic of the interpolation computing is the fact that it is time-consuming. In paper, we present two cheap approaches of HPC. The first solution is a utilization of graphics processing units (GPUs) where the availability of enormous computational performance of easily programmable GPUs can rapidly decrease time of computing. The second one is a utilization of multi-thread CPUs. In our article we demonstrate how t!
o deploy the CUDA architecture, which utilizes the powerful parallel computation capacity of GPU, to accelerate computational process of snow cover depth using the inverse-distance weighting (IDW) method. The performance of GPU we face with OpenMP implementation of IDW method. We consider variable number of threads per CPU. The outputs are visualized by the GIS Grass tool
Keywords: GPGPU, CUDA, Multi-core processor, OpenMP, Snow cover depth, Interpolation
EXTENSION of the file: .pdf
Special (Invited) Session: Design and Performance Evaluation of Snow Cover Computing on GPUs
Organizer of the Session: 646-707
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