Advanced Computational Infrastructures for Parallel and Distributed Applications

* Advanced computational infrastructures provide programming, execution and runtime management support for large-scale adaptive implementations. * Investigates the design, architectures and implementations of advanced computional infrastructures.

This unique, cross-disciplinary reference investigates the state of the art in advanced computational infrastructures and the applications they support. It provides a comprehensive discussion of the requirements, challenges, underlying philosophies, and deployment of infrastructures that provide programming, execution, and runtime management for large-scale adaptive implementations. The information is presented from the perspective of system architects, software engineers, computational scientists, and applications scientists, making the book useful for practitioners and students in these fields.

Preface. Contributors. Biographies. 1. Introduction: Enabling Large-Scale Computational Science-Motivations, Requirements, and Challenges (Manish Parashar and Xiaolin Li). Part I Adaptive Applications in Science and Engineering. 2. Adaptive Mesh Refinement MHD Simulations of Tokamak Refueling (Ravi Samtaney). 3. Parallel Computing Engines for Subsurface Imaging Technologies (Tian-Chyi J. Yeh, Xing Cai, Hans P. Langtangen, Junfeng Zhu, and Chuen-Fa Ni). 4. Plane Wave Seismic Data: Parallel and Adaptive Strategies for Velocity Analysis and Imaging (Paul L. Stoffa, Mrinal K. Sen, Roustam K. Seif, and Reynam C. Pestana). 5. Data-Directed Importance Sampling for Climate Model Parameter Uncertainty Estimation (Charles S. Jackson, Mrinal K. Sen, Paul L. Stoffa, and Gabriel Huerta). 6. Adaptive Cartesian Methods for Modeling Airborne Dispersion (Andrew Wissink, Branko Kosovic, Marsha Berger, Kyle Chand, and Fotini K. Chow). 7. Parallel and Adaptive Simulation of Cardiac Fluid Dynamics (Boyce E. Griffith, Richard D. Hornung, David M. McQueen, and Charles S. Peskin). 8. Quantum Chromodynamics on the BlueGene/L Supercomputer (Pavlos M. Vranas and Gyan Bhanot). Part II Adaptive Computational Infrastructures. 9. The SCIJump Framework for Parallel and Distributed Scientific Computing (Steven G. Parker, Kostadin Damevski, Ayla Khan, Ashwin Swaminathan, and Christopher R. Johnson). 10. Adaptive Computations in the Uintah Framework (Justin Luitjens, James Guilkey, Todd Harman, Bryan Worthen, and Steven G. Parker) 11. Managing Complexity in Massively Parallel, Adaptive, Multiphysics Finite Element Applications (Harold C. Edwards). 12. GrACE: Grid Adaptive Computational Engine for Parallel Structured AMR Applications (Manish Parashar and Xiaolin Li). 13. Charm++ and AMPI: Adaptive Runtime Strategies via Migratable Objects (Laxmikant V. Kale and Gengbin Zheng). 14. The Seine Data Coupling Framework for Parallel Scientific Applications (Li Zhang, Ciprian Docan, and Manish Parashar). Part III Dynamic Partitioning and Adaptive Runtime Management Frameworks. 15. Hypergraph-Based Dynamic Partitioning and Load Balancing (Umit V. Catalyurek, Doruk Bozda&
, Erik G. Boman, Karen D. Devine, Robert Heaphy, and Lee A. Riesen). 16. Mesh Partitioning for Efficient Use of Distributed Systems (Jian Chen and Valerie E. Taylor). 17. Variable Partition Inertia: Graph Repartitioning and Load Balancing for Adaptive Meshes (Chris Walshaw). 18. A Hybrid and Flexible Data Partitioner for Parallel SAMR (Johan Steensland). 19. Flexible Distributed Mesh Data Structure for Parallel Adaptive Analysis (Mark S. Shephard and Seegyoung Seol). 20. HRMS: Hybrid Runtime Management Strategies for Large-Scale Parallel Adaptive Applications (Xiaolin Li and Manish Parashar). 21. Physics-Aware Optimization Method (Yeliang Zhang and Salim Hariri). 22. DistDLB: Improving Cosmology SAMR Simulations on Distributed Computing Systems Through Hierarchical Load Balancing (Zhiling Lan, Valerie E. Taylor, and Yawei Li). Index.