Designing Scientific Applications on GPUs

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.

"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

• PRESENTATION OF GPUs: Presentation of the GPU Architecture and the Cuda Environment. Introduction to Cuda. IMAGE PROCESSING: Setting up the Environment. Implementing a Fast Median Filter. Implementing an Efficient Convolution Operation on GPU. SOFTWARE DEVELOPMENT: Development of Software Components for Heterogeneous Many-Core Architectures. Development Methodologies for GPU and Cluster of GPUs. OPTIMIZATION: GPU-Accelerated Tree-Based Exact Optimization Methods. Parallel GPU-Accelerated Metaheuristics. Linear Programming on a GPU: A Case Study. NUMERICAL APPLICATIONS: Fast Hydrodynamics on Heterogeneous Many-Core Hardware. Parallel Monotone Spline Interpolation and Approximation on GPUs. Solving Linear Systems with GMRES and CG Methods on GPU Clusters. Solving Sparse Nonlinear Systems of Obstacle Problems on GPU Clusters. Ludwig: Multiple GPUs for a Fluid Lattice Boltzmann Application. Numerical Validation and GPU Performance in Atomic Physics. GPU-Accelerated Envelope-Following Method. OTHER: Implementing Multi-Agent Systems on GPU. Pseudorandom Number Generator on GPU. Solving Large Sparse Linear Systems for Integer Factorization on GPUs. Index.