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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010329122 | QA76.76.A65 D47 2014 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Author Notes
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.
Table of Contents
| Presentation of the GPU Architecture |
| Pseudo Random Number Generator on GPU |
| Fast Kernels for Image and Signal Processing |
| Region-Based Algorithm for Large Images Segmentation on GPU |
| On the Development of High-Performance Software Library for Emerging Architectures: Design and Analysis |
| GPU-Accelerated Tree-Based Exact Optimization Methods |
| Parallel Meta-Heuristics for Solving Challenging Problems on GPU Accelerators |
| Linear Programming on a GPU: A Study Case Based on the Simplex Method and the Branch-Cut-and-Bound Algorithm |
| Pertinence and Development Methodologies for GPU and Cluster of GPU |
| Sparse Linear System Solvers with the GMRES Method on GPU Clusters |
| Parallel Solution of the Obstacle Problem on GPU Clusters |
| Complex Fluid Lattice Boltzmann on GPU Clusters |
| Deployment on GPU of an Atomic Physics Program |
| Fast GPU-Accelerated Desktop Application |
| GPU-Based Envelop-Follow Simulation Techniques for Power Converters Design |
| Domain Decomposition Method on GPU Architecture |
| Solving Large Sparse Linear Systems for Integer Factorization on GPUs |
| Performing Large-Scale Robust Regression on GPUs |
