Exploring new variations of classical methods as well as recent approaches appearing in the field, Computational Fluid Dynamics demonstrates the extensive use of numerical techniques and mathematical models in fluid mechanics. It presents various numerical methods, including finite volume, finite difference, finite element, spectral, smoothed particle hydrodynamics (SPH), mixed-element-volume, and free surface flow. Taking a unified point of view, the book first introduces the basis of finite volume, weighted residual, and spectral approaches. The contributors present the SPH method, a novel approach of computational fluid dynamics based on the mesh-free technique, and then improve the method using an arbitrary Lagrange Euler (ALE) formalism. They also explain how to improve the accuracy of the mesh-free integration procedure, with special emphasis on the finite volume particle method (FVPM). After describing numerical algorithms for compressible computational fluid dynamics, the text discusses the prediction of turbulent complex flows in environmental and engineering problems. The last chapter explores the modeling and numerical simulation of free surface flows, including future behaviors of glaciers. The diverse applications discussed in this book illustrate the importance of numerical methods in fluid mechanics. With research continually evolving in the field, there is no doubt that new techniques and tools will emerge to offer greater accuracy and speed in solving and analyzing even more fluid flow problems.

A Thorough Overview of the Next Generation in Computing Poised to follow in the footsteps of the Internet, grid computing is on the verge of becoming more robust and accessible to the public in the near future. Focusing on this novel, yet already powerful, technology, Introduction to Grid Computing explores state-of-the-art grid projects, core grid technologies, and applications of the grid. After comparing the grid with other distributed systems, the book covers two important aspects of a grid system: scheduling of jobs and resource discovery and monitoring in grid. It then discusses existing and emerging security technologies, such as WS-Security and OGSA security, as well as the functions of grid middleware at a conceptual level. The authors also describe famous grid projects, demonstrate the pricing of European options through the use of the Monte Carlo method on grids, and highlight different parallelization possibilities on the grid. Taking a tutorial approach, this concise book provides a complete introduction to the components of the grid architecture and applications of grid computing. It expertly shows how grid computing can be used in various areas, from computational mechanics to risk management in financial institutions.

Exploring new variations of classical methods as well as recent approaches appearing in the field, Computational Fluid Dynamics demonstrates the extensive use of numerical techniques and mathematical models in fluid mechanics. It presents various numerical methods, including finite volume, finite difference, finite element, spectral, smoothed particle hydrodynamics (SPH), mixed-element-volume, and free surface flow. Taking a unified point of view, the book first introduces the basis of finite volume, weighted residual, and spectral approaches. The contributors present the SPH method, a novel approach of computational fluid dynamics based on the mesh-free technique, and then improve the method using an arbitrary Lagrange Euler (ALE) formalism. They also explain how to improve the accuracy of the mesh-free integration procedure, with special emphasis on the finite volume particle method (FVPM). After describing numerical algorithms for compressible computational fluid dynamics, the text discusses the prediction of turbulent complex flows in environmental and engineering problems. The last chapter explores the modeling and numerical simulation of free surface flows, including future behaviors of glaciers. The diverse applications discussed in this book illustrate the importance of numerical methods in fluid mechanics. With research continually evolving in the field, there is no doubt that new techniques and tools will emerge to offer greater accuracy and speed in solving and analyzing even more fluid flow problems.

As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis software. Addressing performance issues, Cloud Computing: Data-Intensive Computing and Scheduling explores the evolution of classical techniques and describes completely new methods and innovative algorithms. The book delineates many concepts, models, methods, algorithms, and software used in cloud computing. After a general introduction to the field, the text covers resource management, including scheduling algorithms for real-time tasks and practical algorithms for user bidding and auctioneer pricing. It next explains approaches to data analytical query processing, including pre-computing, data indexing, and data partitioning. Applications of MapReduce, a new parallel programming model, are then presented. The authors also discuss how to optimize multiple group-by query processing and introduce a MapReduce real-time scheduling algorithm. A useful reference for studying and using MapReduce and cloud computing platforms, this book presents various technologies that demonstrate how cloud computing can meet business requirements and serve as the infrastructure of multidimensional data analysis applications.

The integration and convergence of state-of-the-art technologies in the grid have enabled more flexible, automatic, and complex grid services to fulfill industrial and commercial needs, from the LHC at CERN to meteorological forecasting systems. Fundamentals of Grid Computing: Theory, Algorithms and Technologies discusses how the novel technologies of semantic web and workflow have been integrated into the grid and grid services. The book explains how distributed mutual exclusion algorithms offer solutions to transmission and control processes. It also addresses the replication problem in data grids with limited replica storage and the problem of data management in grids. After comparing utility, grid, autonomic, and cloud computing, the book presents efficient solutions for the reliable execution of applications in computational grid platforms. It then describes a fault tolerant distributed scheduling algorithm for large-scale distributed applications, along with broadcasting algorithms for institutional grids. The final chapter shows how load balancing is integrated into a real-world scientific application. Helping readers develop practical skills in grid technology, the appendices introduce user-friendly open source software written in Java. One of the software packages covers strategies for data replication in the grid. The other deals with the implementation of a simulator for distributed scheduling in grid environments. The various technology presented in this book demonstrates the wide aspects of interest in grid computing as well as the many possibilities and venues that exist in this research area. This interest will only further evolve as numerous exciting developments still await us.

As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis software. Addressing performance issues, Cloud Computing: Data-Intensive Computing and Scheduling explores the evolution of classical techniques and describes completely new methods and innovative algorithms. The book delineates many concepts, models, methods, algorithms, and software used in cloud computing. After a general introduction to the field, the text covers resource management, including scheduling algorithms for real-time tasks and practical algorithms for user bidding and auctioneer pricing. It next explains approaches to data analytical query processing, including pre-computing, data indexing, and data partitioning. Applications of MapReduce, a new parallel programming model, are then presented. The authors also discuss how to optimize multiple group-by query processing and introduce a MapReduce real-time scheduling algorithm. A useful reference for studying and using MapReduce and cloud computing platforms, this book presents various technologies that demonstrate how cloud computing can meet business requirements and serve as the infrastructure of multidimensional data analysis applications.

Research and development in scientific computing and computational science has considerably increased the power of numerical simulation. Engineers and researchers are now able to solve large and complex problems which were impossible to solve in the past. This new book presents some techniques, methods and algorithms for solving engineering problems arising in energy and environment applications.

A Thorough Overview of the Next Generation in Computing Poised to follow in the footsteps of the Internet, grid computing is on the verge of becoming more robust and accessible to the public in the near future. Focusing on this novel, yet already powerful, technology, Introduction to Grid Computing explores state-of-the-art grid projects, core grid technologies, and applications of the grid. After comparing the grid with other distributed systems, the book covers two important aspects of a grid system: scheduling of jobs and resource discovery and monitoring in grid. It then discusses existing and emerging security technologies, such as WS-Security and OGSA security, as well as the functions of grid middleware at a conceptual level. The authors also describe famous grid projects, demonstrate the pricing of European options through the use of the Monte Carlo method on grids, and highlight different parallelization possibilities on the grid. Taking a tutorial approach, this concise book provides a complete introduction to the components of the grid architecture and applications of grid computing. It expertly shows how grid computing can be used in various areas, from computational mechanics to risk management in financial institutions.

As per the constant need to solve larger and larger numerical problems, it is not possible to neglect the opportunity that comes from the close adaptation of computational algorithms and their implementations for particular features of computing devices, i.e. the characteristics and performance of available workstations and servers. In the last decade, the advances in hardware manufacturing, the decreasing cost and the spread of GPUs have attracted the attention of researchers for numerical simulations, given that for some problems, GPU-based simulations can significantly outperform the ones based on CPUs. The objective of this book is first to present how to design in a context of GPGPU numerical methods in order to obtain the highest efficiency. A second objective of this book is to propose new auto-tuning techniques to optimize access on GPU. A third objective of this book is to propose new preconditioning techniques for GPGPU. Finally, an original energy consumption model is proposed, leading to a robust and accurate energy consumption prediction model.