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Designing scientific applications on GPUs /
"This book covers designs of scientific applications for GPUs, beginning with a review of the principles of GPU programming. It then describes various scientific applications for GPUs and presents lessons learned. Scientific applications covered include computations on matrix operations, linear...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton [Florida] :
CRC/Taylor & Francis,
[2014]
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| Colección: | Chapman & Hall/CRC numerical analysis and scientific computing ;
21. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; 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 efficient 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 metaheuristics10. 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 fluid 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 simulationVI. 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.