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. 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.

Many of today’s 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.
This book 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.
The first part of the book introduces the GPUs and Nvidia’s 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.

Features
• Provides a comprehensive, research-level treatment of the use of GPUs for scientific applications
• Explains how to port scientific applications on GPUs
• Discusses the latest algorithms and their use with classical applications
• Describes key elements for choosing the context and way of implementing the algorithms
• Includes code samples on the editor’s website

Contents
List of Figures
List of Tables
Preface
I Presentation of GPUs
1 Presentation of the GPU architecture and of the CUDA environment
2 Introduction to CUDA
II Image processing
3 Setting up the environment
4 Implementing a fast median filter
5 Implementing an ecient convolution operation on GPU
III Software development
6 Development of software components for heterogeneous many-core architectures
7 Development methodologies for GPU and cluster of GPUs
IV Optimization
8 GPU-accelerated tree-based exact optimization methods
9 Parallel GPU-accelerated metaheuristics
10 Linear programming on a GPU: a case study
V Numerical applications
11 Fast hydrodynamics on heterogeneous many-core hardware
12 Parallel monotone spline interpolation and approximation on GPUs
13 Solving sparse linear systems with GMRES and CG methods on GPU clusters
14 Solving sparse nonlinear systems of obstacle problems on GPU clusters
15 Ludwig: multiple GPUs for a complex
uid lattice Boltzmann application
16 Numerical validation and performance optimization on GPUs of an application in atomic physics
17 A GPU-accelerated envelope-following method for switching power converter simulation
VI Other
18 Implementing multi-agent systems on GPU
19 Pseudorandom number generator on GPU
20 Solving large sparse linear systems for integer factorization on GPUs
Index

Download File Size:8.82 MB

---