Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications

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 designarchitectures, and implementations of advanced computationalinfrastructures and the applications they supportEmerging large-scale adaptive scientific and engineeringapplications are requiring an increasing amount of computing andstorage resources to provide new insights into complex systems. Dueto their runtime adaptivity, these applications exhibit complicatedbehaviors that are highly dynamic, heterogeneous, andunpredictable--and therefore require full-fledgedcomputational infrastructure support for problem solving, runtimemanagement, and dynamic partitioning/balancing. This book presentsa comprehensive study of the design, architecture, andimplementation of advanced computational infrastructures as well asthe adaptive applications developed and deployed using theseinfrastructures from different perspectives, including systemarchitects, software engineers, computational scientists, andapplication scientists. Providing insights into recent researchefforts and projects, the authors include descriptions andexperiences pertaining to the realistic modeling of adaptiveapplications on parallel and distributed systems.The first part of the book focuses on high-performance adaptivescientific applications and includes chapters that describehigh-impact, real-world application scenarios in order to motivatethe need for advanced computational engines as well as to outlinetheir requirements. The second part identifies popular and widelyused adaptive computational infrastructures. The third part focuseson the more specific partitioning and runtime management schemesunderlying these computational toolkits.* Presents representative problem-solving environments andinfrastructures, runtime management strategies, partitioning anddecomposition methods, and adaptive and dynamic applications* Provides a unique collection of selected solutions andinfrastructures that have significant impact with sufficientintroductory materials* Includes descriptions and experiences pertaining to therealistic modeling of adaptive applications on parallel anddistributed systemsThe cross-disciplinary approach of this reference delivers acomprehensive discussion of the requirements, design challengesunderlying design philosophies, architectures, andimplementation/deployment details of advanced computationalinfrastructures. It makes it a valuable resource for advancedcourses in computational science and software/systems engineeringfor senior undergraduate and graduate students, as well as forcomputational and computer scientists, software developers, andother industry professionals.

Preface xiContributors xvBiographies xix1. Introduction: Enabling Large-Scale Computational Science--Motivations, Requirements, andChallenges 1Manish Parashar and Xiaolin LiPart I Adaptive Applications in Science and Engineering2. Adaptive Mesh Refinement MHD Simulations of Tokamak Refueling 11Ravi Samtaney3. Parallel Computing Engines for Subsurface Imaging Technologies 29Tian-Chyi J. Yeh, Xing Cai, Hans P. Langtangen, Junfeng Zhu, and Chuen-Fa Ni4. PlaneWave Seismic Data: Parallel and Adaptive Strategies for Velocity Analysis and Imaging 45Paul L. Stoffa, Mrinal K. Sen, Roustam K. Seif, and Reynam C. Pestana5. Data-Directed Importance Sampling for Climate Model Parameter Uncertainty Estimation 65Charles S. Jackson, Mrinal K. Sen, Paul L. Stoffa, and Gabriel Huerta6. Adaptive Cartesian Methods for Modeling Airborne Dispersion 79Andrew Wissink, Branko Kosovic, Marsha Berger, Kyle Chand, and Fotini K. Chow7. Parallel and Adaptive Simulation of Cardiac Fluid Dynamics 105Boyce E. Griffith, Richard D. Hornung, David M. McQueen, and Charles S. Peskin8. Quantum Chromodynamics on the BlueGene/L Supercomputer 131Pavlos M. Vranas and Gyan BhanotPart II Adaptive Computational Infrastructures9. The SCIJump Framework for Parallel and Distributed Scientific Computing 151Steven G. Parker, Kostadin Damevski, Ayla Khan, Ashwin Swaminathan, and Christopher R. Johnson10. Adaptive Computations in the Uintah Framework 171Justin Luitjens, James Guilkey, Todd Harman, Bryan Worthen, and Steven G. Parker11. Managing Complexity in Massively Parallel, Adaptive, Multiphysics Finite Element Applications 201Harold C. Edwards12. GrACE: Grid Adaptive Computational Engine for Parallel Structured AMR Applications 249Manish Parashar and Xiaolin Li13. Charm++ and AMPI: Adaptive Runtime Strategies via Migratable Objects 265Laxmikant V. Kale and Gengbin Zheng14. The Seine Data Coupling Framework for Parallel Scientific Applications 283Li Zhang, Ciprian Docan, and Manish ParasharPart III Dynamic Partitioning and Adaptive Runtime Management Frameworks15. Hypergraph-Based Dynamic Partitioning and Load Balancing 313Umit V. Catalyurek, Doruk Bozda?g, Erik G. Boman, Karen D. Devine, Robert Heaphy, and Lee A. Riesen16. Mesh Partitioning for Efficient Use of Distributed Systems 335Jian Chen and Valerie E. Taylor17. Variable Partition Inertia: Graph Repartitioning and Load Balancing for Adaptive Meshes 357Chris Walshaw18. A Hybrid and Flexible Data Partitioner for Parallel SAMR 381Johan Steensland19. Flexible Distributed Mesh Data Structure for Parallel Adaptive Analysis 407Mark S. Shephard and Seegyoung Seol20. HRMS: Hybrid Runtime Management Strategies for Large-Scale Parallel Adaptive Applications 437Xiaolin Li and Manish Parashar21. Physics-Aware Optimization Method 463Yeliang Zhang and Salim Hariri22. DistDLB: Improving Cosmology SAMR Simulations on Distributed Computing Systems Through Hierarchical Load Balancing 479Zhiling Lan, Valerie E. Taylor, and Yawei LiIndex 503