Designing Scientific Applications on GPUs

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.

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.

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.