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E-raamat: Designing Scientific Applications on GPUs

Edited by (University of Franche-Comte, Belfort, France)
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Many of todays complex scientific applications now require a vast amount of computational power. General purpose graphics processing units (GPGPUs) enable researchers in a variety of fields to benefit from the computational power of all the cores available inside graphics cards.

Understand the Benefits of Using GPUs for Many Scientific Applications

Designing Scientific Applications on GPUs shows you how to use GPUs for applications in diverse scientific fields, from physics and mathematics to computer science. The book explains the methods necessary for designing or porting your scientific application on GPUs. It will improve your knowledge about image processing, numerical applications, methodology to design efficient applications, optimization methods, and much more.

Everything You Need to Design/Port Your Scientific Application on GPUs

The first part of the book introduces the GPUs and Nvidias CUDA programming model, currently the most widespread environment for designing GPU applications. The second part focuses on significant image processing applications on GPUs. The third part presents general methodologies for software development on GPUs and the fourth part describes the use of GPUs for addressing several optimization problems. The fifth part covers many numerical applications, including obstacle problems, fluid simulation, and atomic physics models. The last part illustrates agent-based simulations, pseudorandom number generation, and the solution of large sparse linear systems for integer factorization. Some of the codes presented in the book are available online.

Arvustused

"This book covers not only the knowledge of GPU and CUDA programming, but also provides successful real applications in many domains, including signal processing, image processing, physics, and artificial intelligence. The most recent research outcome and the most recent progress of GPU architectures are included, such as multi-GPU programming and GPU clusters. I believe it is a very good reference for GPU and CUDA parallel programming courses as it provides detailed illustration of the architectures of GPU, programming principles of CUDA, CUDA libraries for algebra, and a series of real applications. In addition, it will definitely contribute to the progress of research in CUDA-enabled parallel computing." Professor Ying Liu, School of Computer and Control, University of Chinese Academy of Sciences

List of Figures
xi
List of Tables
xvii
Preface xxi
I Presentation of GPUs
1(22)
1 Presentation of the GPU architecture and of the CUDA environment
3(10)
Raphael Couturier
2 Introduction to CUDA
13(10)
Raphael Couturier
II Image processing
23(48)
3 Setting up the environment
25(6)
Gilles Perrot
4 Implementing a fast median filter
31(22)
Gilles Perrot
5 Implementing an efficient convolution operation on GPU
53(18)
Gilles Perrot
III Software development
71(80)
6 Development of software components for heterogeneous many-core architectures
73(32)
Stefan L. Glimberg
Allan P. Engsig-Karup
Allan S. Nielsen
Bernd Dammann
7 Development methodologies for GPU and cluster of GPUs
105(46)
Sylvain Contassot-Vivier
Stephane Vialle
Jens Gustedt
IV Optimization
151(98)
8 GPU-accelerated tree-based exact optimization methods
153(30)
Imen Chakroun
Nouredine Melab
9 Parallel GPU-accelerated metaheuristics
183(32)
Malika Mehdi
Ahcene Bendjoudi
Lakhdar Loukil
Nouredine Melab
10 Linear programming on a GPU: a case study
215(34)
Xavier Meyer
Bastien Chopard
Paul Albuquerque
V Numerical applications
249(164)
11 Fast hydrodynamics on heterogeneous many-core hardware
251(44)
Allan P. Engsig-Karup
Stefan L. Glimberg
Allan S. Nielsen
Ole Lindberg
12 Parallel monotone spline interpolation and approximation on GPUs
295(16)
Gleb Beliakov
Shaowu Liu
13 Solving sparse linear systems with GMRES and CG methods on GPU clusters
311(20)
Lilia Ziane Khodja
Raphael Couturier
Jacques Bahi
14 Solving sparse nonlinear systems of obstacle problems on GPU clusters
331(24)
Lilia Ziane Khodja
Raphael Couturier
Jacques Bahi
Ming Chau
Pierre Spiteri
15 Ludwig: multiple GPUs for a complex fluid lattice Boltzmann application
355(16)
Alan Gray
Kevin Stratford
16 Numerical validation and performance optimization on GPUs of an application in atomic physics
371(24)
Rachid Habel
Pierre Fortin
Fabienne Jezeauel
Jean-Luc Lamotte
Stan Scott
17 A GPU-accelerated envelope-following method for switching power converter simulation
395(18)
Xuexin Liu
Sheldon Xiang-Dong Tan
Hai Wang
Hao Yu
VI Other
413(60)
18 Implementing multi-agent systems on GPU
415(26)
Guillaume Laville
Christophe Lang
Benedicte Herrmann
Laurent Philippe
Kamel Mazouzi
Nicolas Marilleau
19 Pseudorandom number generator on GPU
441(12)
Raphael Couturier
Christophe Guyeux
20 Solving large sparse linear systems for integer factorization on GPUs
453(20)
Bertil Schmidt
Hoang-Vu Dang
Index 473
Raphaël Couturier is a professor of computer science at the University of Franche-Comte and vice head of the Computer Science Department at FEMTO-ST Institute. He has co-authored over 80 articles in peer-reviewed journals and conferences. He received a Ph.D. from Henri Poincaré University. His research interests include parallel and distributed computation, numerical algorithms, GPU and FPGA computing, and asynchronous iterative algorithms.