Designing Scientific Applications on GPUs

Designing Scientific Applications on GPUs

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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. 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 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.
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Product details

  • Hardback | 498 pages
  • 162 x 236 x 32mm | 859.99g
  • Taylor & Francis Inc
  • CRC Press Inc
  • Bosa Roca, United States
  • English
  • 118 black & white illustrations, 46 black & white tables
  • 1466571624
  • 9781466571624
  • 1,901,399

Review quote

"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
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About Raphael Couturier

Raphael 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 Poincare University. His research interests include parallel and distributed computation, numerical algorithms, GPU and FPGA computing, and asynchronous iterative algorithms.
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Table of contents

PRESENTATION OF GPUs Presentation of the GPU Architecture and the Cuda Environment Raphael Couturier Introduction Brief history of video card GPGPU Architecture of current GPUs Kinds of parallelism Cuda multithreading Memory hierarchy Introduction to Cuda Raphael Couturier Introduction First example Second example: using CUBLAS Third example: matrix-matrix multiplication IMAGE PROCESSING Setting up the Environment Gilles Perrot Data transfers, memory management Performance measurements Implementing a Fast Median Filter Gilles Perrot Introduction Median filtering NVidia GPU tuning recipes A 3x3 median filter: using registers A 5x5 and more median filter Implementing an Efficient Convolution Operation on GPU Gilles Perrot Overview Definition Implementation Separable convolution SOFTWARE DEVELOPMENT Development of Software Components for Heterogeneous Many-Core Architectures Stefan L. Glimberg, Allan P. Engsig-Karup, Allan S. Nielsen, and Bernd Dammann Software development for heterogeneous Heterogeneous library design for PDE solvers Model problems Optimization strategies for multi-GPU systems Development Methodologies for GPU and Cluster of GPUs Sylvain Contassot-Vivier, Stephane Vialle, and Jens Gustedt Introduction General scheme of synchronous code with computation/communication overlapping in GPU clusters General scheme of asynchronous parallel code with computation/communication overlapping Perspective: A unifying programming model OPTIMIZATION GPU-Accelerated Tree-Based Exact Optimization Methods Imen Chakroun and Nouredine Melab Introduction Branch-and-bound (B&B) algorithm Parallel B&B algorithms The flowshop scheduling problem GPU-accelerated B&B based on the parallel tree exploration (GPU-PTE-BB) GPU-accelerated B&B based on the parallel evaluation of bounds (GPU-PEB-BB) Thread divergence Memory access optimization Experiments Parallel GPU-Accelerated Metaheuristics Malika Mehdi, Ahcene Bendjoudi, Lakhdar Loukil, and Nouredine Melab Introduction Combinatorial optimization Parallel models for metaheuristics Challenges for the design of GPU-based metaheuristics State-of-the-art parallel metaheuristics on GPUs Frameworks for metaheuristics on GPUs Case study: Accelerating large neighborhood LS method on GPUs for solving the Q3AP Linear Programming on a GPU: A Case Study Xavier Meyer, Bastien Chopard, and Paul Albuquerque Introduction Simplex algorithm B&B algorithm CUDA considerations Implementations Performance model Measurements and analysis NUMERICAL APPLICATIONS Fast Hydrodynamics on Heterogeneous Many-Core Hardware Allan P. Engsig-Karup, Stefan L. Glimberg, Allan S. Nielsen, and Ole Lindberg On hardware trends and challenges in scientific applications On modeling paradigms for highly nonlinear and dispersive water waves Governing equations The numerical model Properties of the numerical model Numerical experiments Parallel Monotone Spline Interpolation and Approximation on GPUs Gleb Beliakov and Shaowu Liu Introduction Monotone splines Smoothing noisy data via parallel isotone regression Solving Linear Systems with GMRES and CG Methods on GPU Clusters Lilia Ziane Khodja, Raphael Couturier, and Jacques Bahi Introduction Krylov iterative methods Parallel implementation on a GPU cluster Experimental results Solving Sparse Nonlinear Systems of Obstacle Problems on GPU Clusters Lilia Ziane Khodja, Raphael Couturier, Jacques Bahi, Ming Chau, and Pierre Spiteri Introduction Obstacle problems Parallel iterative method Parallel implementation on a GPU cluster Experimental tests on a GPU cluster Red-black ordering technique Ludwig: Multiple GPUs for a Fluid Lattice Boltzmann Application Alan Gray and Kevin Stratford Introduction Background Single GPU implementation Multiple GPU implementation Moving solid particles Numerical Validation and GPU Performance in Atomic Physics Rachid Habel, Pierre Fortin, Fabienne Jezequel, Jean-Luc Lamotte, and Stan Scott Introduction 2DRMP and the PROP program Numerical validation of PROP in single precision Toward a complete deployment of PROP on GPUs Performance results Propagation of multiple concurrent energies on GPU GPU-Accelerated Envelope-Following Method Xuexin Liu, Sheldon Xiang-Dong Tan, Hai Wang, and Hao Yu Introduction The envelope-following method in a nutshell New parallel envelope-following method Numerical examples OTHER Implementing Multi-Agent Systems on GPU Guillaume Laville, Christophe Lang, Benedicte Herrmann, Laurent Philippe, Kamel Mazouzi, and Nicolas Marilleau Introduction Running agent-based simulations A first practical example Second example Analysis and recommendations Pseudorandom Number Generator on GPU Raphael Couturier and Christophe Guyeux Introduction Basic reminders Toward efficiency and improvement for CI PRNG Experiments Solving Large Sparse Linear Systems for Integer Factorization on GPUs Bertil Schmidt and Hoang-Vu Dang Introduction Block Wiedemann algorithm SpMV OVER GF(2) for NFS matrices using existing formats on GPUs A hybrid format for SpMV on GPUs SCOO for single-precision floating-point matrices Performance evaluation Index A Bibliography appears at the end of each chapter.
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