Advanced optimization techniques for MT simulation on GPUs | Using massively parallel devices for scientific computing

Eyad Hailat

ISBN 10: 3659554634 / ISBN 13: 9783659554636

Published by LAP LAMBERT Academic Publishing, 2014

About this Item

Advanced optimization techniques for MT simulation on GPUs | Using massively parallel devices for scientific computing | Eyad Hailat | Taschenbuch | 132 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659554636 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Advanced optimization techniques for MT simulation on GPUs | Using massively parallel devices for scientific computing
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 3659554634
• ISBN 13: 9783659554636
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 215 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The objective of this work is to design and implement a self-adaptive parallel GPU optimized Monte Carlo algorithm for the simulation of adsorption in porous materials. We focus on Nvidia's GPUs and CUDA's Fermi architecture specifically. The resulting package supports the different ensemble methods for the Monte Carlo simulation, which will allow for the simulation of multi-component adsorption in porous solids. Such an algorithm will have broad applications to the development of novel porous materials for the sequestration of CO2 and the filtration of toxic industrial chemicals. The primary objective of this work is the release of a massively parallel open source Monte Carlo simulation engine implemented using GPUs, called GOMC. The code will utilize the canonical ensemble, and the Gibbs ensemble method, which will allow for the simulation of multiple phenomena, including liquid-vapor phase coexistence, and single and multi-component adsorption in porous materials. In addition, the grand canonical ensemble and the configurational-bias algorithms have been implemented so that polymeric materials and small proteins may be simulated.

About the Author

Eyad Hailat earned his Ph.D. in Computer Science from Wayne State University in 2013. His research interest includes High Performance Computing for massively parallel devices, such as the GPU. One application for his research is numerical simulations to accurately predict properties of materials and their guest-adsorption characteristics.