As per the constant need to solve larger and larger numerical problems, it is not possible to neglect the opportunity that comes from the close adaptation of computational algorithms and their implementations for particular features of computing devices, i.e. the characteristics and performance of available workstations and servers. In the last decade, the advances in hardware manufacturing, the decreasing cost and the spread of GPUs have attracted the attention of researchers for numerical simulations, given that for some problems, GPU-based simulations can significantly outperform the ones based on CPUs. The objective of this book is first to present how to design in a context of GPGPU numerical methods in order to obtain the highest efficiency. A second objective of this book is to propose new auto-tuning techniques to optimize access on GPU. A third objective of this book is to propose new preconditioning techniques for GPGPU. Finally, an original energy consumption model is proposed, leading to a robust and accurate energy consumption prediction model.
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Our book mainly targets Engineers and Professionals working in all fields involving numerical simulation of large problems (mechanical and chemical Engineering, mechanics, fluid dynamics, acoustics, power systems, biology, etc.) and dealing with parallel systems. This book will also be of interest to Academics, Researchers and Students who wish to address scientific computing with Graphics Processing Units technology.
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Maße
Höhe: 229 mm
Breite: 152 mm
ISBN-13
978-1-78548-244-1 (9781785482441)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Frederic Magoules graduated with a B.Sc. in Engineering Sciences in 1993, a M.Sc. in Applied Mathematics in 1994 and a M.Sc. in Numerical Analysis in 1995, from Universite Pierre & Marie Curie. He received his Ph.D. in Applied Mathematics from Universite Pierre Marie Curie in 2000. He then post-doc'ed and taught there for one year, as Assistant Professor of Numerical Analysis, prior to joining Universite Henri Poincare in 2000 as Assistant and then Associate Professor of Applied Mathematics and Engineering. He received his HDR (Habilitation a Diriger des Recherches) from Universite Pierre & Marie Curie in 2005. He joined Ecole Centrale des Arts et Manufactures in 2006 as Professor of Applied Mathematics (in 2015, Ecole Centrale des Arts et Manufactures merged with Supelec and became CentraleSupelec, Universite Paris-Saclay).
With backgrounds in Computational Science and Engineering, Applied Mathematics, Computer Science, and consulting experience with industry and national laboratories, Frederic Magoules works at the algorithmic interface between parallel computing and the numerical analysis of partial differential equations and algebraic differential equations. Frederic Magoules and his research group design, analyze, develop, and validate mathematical models and computational methods for the high-performance simulation of multidisciplinary scientific and engineering problems.
Author or co-author of over 150 refeered publications in Computational Science and Engineering, Applied Mathematics, and Computer Science, Frederic Magoules has authored 11 books, and edited 9 books. Abal-Kassim Cheik Ahamed received his French Baccalaureat S. (Scientific) in Mayotte, France, in 2006. In 2008, he graduated with three B.Sc. Degree at the Universite de Pau et des Pays de l'Adour and Universite de Cergy- Pontoise, France. He graduated with Engineer degree in the Department of HPC and Scientific Computing at the Ecole Internationale des Sciences du traitement de l'information (EISTI), France, in 2011. He also graduated with a M.Sc. degree in the Department of Science, Technology and Health at the Universite de Cergy-Pontoise, France, in 2011. In 2015, he graduated with PhD degree in applied mathematics and computer science at Ecole Centrale Paris (Applied Mathematics and Systems Laboratory), Chatenay-Malabry, France. Thesis: Numerical methods for the accelerated resolution of large scale linear systems on massively parallel hybrid architectures, Univ. de Franche Comte; Mr. Che-Lun Hung, Providence Univ. His major research interests are oriented to the acceleration of numerical methods using both GPU Computing and Parallel Computing.Currently, he is a postdoctoral fellow at CentraleSupelec, Universite Paris-Saclay, France. He is currently assistant professor at CentraleSupelec for different courses: Introduction to databases, Algorithms, Advanced Programming and software development, and diverse projects.
Autor*in
Ecole Centrale de Paris, France
Ecole Centrale de Paris, France
1. GPU Computing
2. Basics of numerical matrix analysis
3. Linear Algebra Operations
4. Direct Methods
5. Iterative Methods
6. Parallel Iterative Methods
7. Parallel Preconditioning Methods
8. Energy Consumption
