Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications (Wiley Series on Parallel and Distributed Computing)

A unique investigation of the state of the art in design, architectures, and implementations of advanced computational infrastructures and the applications they support Emerging large-scale adaptive scientific and engineering applications are requiring an increasing amount of computing and storage resources to provide new insights into complex systems. Due to their runtime adaptivity, these applications exhibit complicated behaviors that are highly dynamic, heterogeneous, and unpredictable—and therefore require full-fledged computational infrastructure support for problem solving, runtime management, and dynamic partitioning/balancing. This book presents a comprehensive study of the design, architecture, and implementation of advanced computational infrastructures as well as the adaptive applications developed and deployed using these infrastructures from different perspectives, including system architects, software engineers, computational scientists, and application scientists. Providing insights into recent research efforts and projects, the authors include descriptions and experiences pertaining to the realistic modeling of adaptive applications on parallel and distributed systems. The first part of the book focuses on high-performance adaptive scientific applications and includes chapters that describe high-impact, real-world application scenarios in order to motivate the need for advanced computational engines as well as to outline their requirements. The second part identifies popular and widely used adaptive computational infrastructures. The third part focuses on the more specific partitioning and runtime management schemes underlying these computational toolkits. Presents representative problem-solving environments and infrastructures, runtime management strategies, partitioning and decomposition methods, and adaptive and dynamic applications Provides a unique collection of selected solutions and infrastructures that have significant impact with sufficient introductory materials Includes descriptions and experiences pertaining to the realistic modeling of adaptive applications on parallel and distributed systems The cross-disciplinary approach of this reference delivers a comprehensive discussion of the requirements, design challenges, underlying design philosophies, architectures, and implementation/deployment details of advanced computational infrastructures. It makes it a valuable resource for advanced courses in computational science and software/systems engineering for senior undergraduate and graduate students, as well as for computational and computer scientists, software developers, and other industry professionals.

A unique investigation of the state of the art in design, architectures, and implementations of advanced computational infrastructures and the applications they support

Emerging large-scale adaptive scientific and engineering applications are requiring an increasing amount of computing and storage resources to provide new insights into complex systems. Due to their runtime adaptivity, these applications exhibit complicated behaviors that are highly dynamic, heterogeneous, and unpredictable—and therefore require full-fledged computational infrastructure support for problem solving, runtime management, and dynamic partitioning/balancing. This book presents a comprehensive study of the design, architecture, and implementation of advanced computational infrastructures as well as the adaptive applications developed and deployed using these infrastructures from different perspectives, including system architects, software engineers, computational scientists, and application scientists. Providing insights into recent research efforts and projects, the authors include descriptions and experiences pertaining to the realistic modeling of adaptive applications on parallel and distributed systems.

The first part of the book focuses on high-performance adaptive scientific applications and includes chapters that describe high-impact, real-world application scenarios in order to motivate the need for advanced computational engines as well as to outline their requirements. The second part identifies popular and widely used adaptive computational infrastructures. The third part focuses on the more specific partitioning and runtime management schemes underlying these computational toolkits.

• Presents representative problem-solving environments and infrastructures, runtime management strategies, partitioning and decomposition methods, and adaptive and dynamic applications
• Provides a unique collection of selected solutions and infrastructures that have significant impact with sufficient introductory materials
• Includes descriptions and experiences pertaining to the realistic modeling of adaptive applications on parallel and distributed systems

The cross-disciplinary approach of this reference delivers a comprehensive discussion of the requirements, design challenges, underlying design philosophies, architectures, and implementation/deployment details of advanced computational infrastructures. It makes it a valuable resource for advanced courses in computational science and software/systems engineering for senior undergraduate and graduate students, as well as for computational and computer scientists, software developers, and other industry professionals.

Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications......Page 4 Contents......Page 10 Preface......Page 14 ACKNOWLEDGMENTS......Page 17 Contributors......Page 18 Biographies......Page 22 1. Introduction: Enabling Large-Scale Computational Science—Motivations, Requirements, and Challenges......Page 24 Part I Adaptive Applications in Science and Engineering......Page 32 2. Adaptive Mesh Refinement MHD Simulations of Tokamak Refueling......Page 34 3. Parallel Computing Engines for Subsurface Imaging Technologies......Page 52 4. Plane Wave Seismic Data: Parallel and Adaptive Strategies for Velocity Analysis and Imaging......Page 68 5. Data-Directed Importance Sampling for Climate Model Parameter Uncertainty Estimation......Page 88 6. Adaptive Cartesian Methods for Modeling Airborne Dispersion......Page 102 7. Parallel and Adaptive Simulation of Cardiac Fluid Dynamics......Page 128 8. Quantum Chromodynamics on the BlueGene/L Supercomputer......Page 154 Part II Adaptive Computational Infrastructures......Page 172 9. The SCIJump Framework for Parallel and Distributed Scientific Computing......Page 174 10. Adaptive Computations in the Uintah Framework......Page 194 11. Managing Complexity in Massively Parallel, Adaptive, Multiphysics Finite Element Applications......Page 224 12. GrACE: Grid Adaptive Computational Engine for Parallel Structured AMR Applications......Page 272 13. Charm++ and AMPI: Adaptive Runtime Strategies via Migratable Objects......Page 288 14. The Seine Data Coupling Framework for Parallel Scientific Applications......Page 306 Part III Dynamic Partitioning and Adaptive Runtime Management Frameworks......Page 334 15. Hypergraph-Based Dynamic Partitioning and Load Balancing......Page 336 16. Mesh Partitioning for Efficient Use of Distributed Systems......Page 358 17. Variable Partition Inertia: Graph Repartitioning and Load Balancing for Adaptive Meshes......Page 380 18. A Hybrid and Flexible Data Partitioner for Parallel SAMR......Page 404 19. Flexible Distributed Mesh Data Structure for Parallel Adaptive Analysis......Page 430 20. HRMS: Hybrid Runtime Management Strategies for Large-Scale Parallel Adaptive Applications......Page 460 21. Physics-Aware Optimization Method......Page 486 22. DistDLB: Improving Cosmology SAMR Simulations on Distributed Computing Systems Through Hierarchical Load Balancing......Page 502 Index......Page 526 "Emerging large-scale adaptive scientific and engineering applications are requiring an increasing amount of computing and storage resources to provide new insights into complex systems. Due to their runtime adaptivity, these applications exhibit complicated behaviors that are highly dynamic, heterogeneous, and unpredictable--and therefore require full-fledged computational infrastructure support for problem solving, runtime management, and dynamic partitioning/balancing. This book presents a comprehensive study of the design, architecture, and implementation of advanced computational infrastructures as well as the adaptive applications developed and deployed using these infrastructures from different perspectives, including system architects, software engineers, computational scientists, and application scientists. Providing insights into recent research efforts and projects, the authors include descriptions and experiences pertaining to the realistic modeling of adaptive applications on parallel and distributed systems. The first part of the book focuses on high-performance adaptive scientific applications and includes chapters that describe high-impact, real-world application scenarios in order to motivate the need for advanced computational engines as well as to outline their requirements. The second part identifies popular and widely used adaptive computational infrastructures. The third part focuses on the more specific partitioning and runtime management schemes underlying these computational toolkits. Presents representative problem-solving environments and infrastructures, runtime management strategies, partitioning and decomposition methods, and adaptive and dynamic applications. Includes descriptions and experiences pertaining to the realistic modeling of adaptive applications on parallel and distributed systems. The cross-disciplinary approach of this reference delivers a comprehensive discussion of the requirements, design challenges, underlying design philosophies, architectures, and implementation/deployment details of advanced computational infrastructures."--Publisher's web site