Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications

• Year: 2010
• Tongue: English
• Leaves: 522
• Category: Library

✦ Synopsis

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.Content:
Chapter 1 Introduction: Enabling Large?Scale Computational Science—Motivations, Requirements, and Challenges (pages 1–7): Manish Parashar and Xiaolin Li
Chapter 2 Adaptive Mesh Refinement MHD Simulations of Tokamak Refueling (pages 9–27): Ravi Samtaney
Chapter 3 Parallel Computing Engines for Subsurface Imaging Technologies (pages 29–43): Tian?Chyi J. Yeh, Xing Cai, Hans P. Langtangen, Junfeng Zhu and Chuen?Fa Ni
Chapter 4 Plane Wave Seismic Data: Parallel and Adaptive Strategies for Velocity Analysis and Imaging (pages 45–63): Paul L. Stoffa, Mrinal K. Sen, Roustam K. Seif and Reynam C. Pestana
Chapter 5 Data?Directed Importance Sampling for Climate Model Parameter Uncertainty Estimation (pages 65–78): Charles S. Jackson, Mrinal K. Sen, Paul L. Stoffa and Gabriel Huerta
Chapter 6 Adaptive Cartesian Methods for Modeling Airborne Dispersion (pages 79–104): Andrew Wissink, Branko Kosovic, Marsha Berger, Kyle Chand and Fotini K. Chow
Chapter 7 Parallel and Adaptive Simulation of Cardiac Fluid Dynamics (pages 105–130): Boyce E. Griffith, Richard D. Hornung, David M. McQueen and Charles S. Peskin
Chapter 8 Quantum Chromodynamics on the BlueGene/L Supercomputer (pages 131–148): Pavlos M. Vranas and Gyan Bhanot
Chapter 9 The SCIJump Framework for Parallel and Distributed Scientific Computing (pages 149–170): Steven G. Parker, Kostadin Damevski, Ayla Khan, Ashwin Swaminathan and Christopher R. Johnson
Chapter 10 Adaptive Computations in the Uintah Framework (pages 171–199): Justin Luitjens, James Guilkey, Todd Harman, Bryan Worthen and Steven G. Parker
Chapter 11 Managing Complexity in Massively Parallel, Adaptive, Multiphysics Finite Element Applications (pages 201–248): Harold C. Edwards
Chapter 12 GrACE: Grid Adaptive Computational Engine for Parallel Structured AMR Applications (pages 249–263): Manish Parashar and Xiaolin Li
Chapter 13 Charm++ and AMPI: Adaptive Runtime Strategies via Migratable Objects (pages 265–282): Laxmikant V. Kale and Gengbin Zheng
Chapter 14 The Seine Data Coupling Framework for Parallel Scientific Applications (pages 283–309): Li Zhang, Ciprian Docan and Manish Parashar
Chapter 15 Hypergraph?Based Dynamic Partitioning and Load Balancing (pages 311–333): Umit V. Catalyurek, Doruk Bozda?g, Erik G. Boman, Karen D. Devine, Robert Heaphy and Lee A. Riesen
Chapter 16 Mesh Partitioning for Efficient Use of Distributed Systems (pages 335–356): Jian Chen and Valerie E. Taylor
Chapter 17 Variable Partition Inertia: Graph Repartitioning and Load Balancing for Adaptive Meshes (pages 357–380): Chris Walshaw
Chapter 18 A Hybrid and Flexible Data Partitioner for Parallel SAMR (pages 381–406): Johan Steensland
Chapter 19 Flexible Distributed Mesh Data Structure for Parallel Adaptive Analysis (pages 407–435): Mark S. Shephard and Seegyoung Seol
Chapter 20 HRMS: Hybrid Runtime Management Strategies for Large?Scale Parallel Adaptive Applications (pages 437–462): Xiaolin Li and Manish Parashar
Chapter 21 Physics?Aware Optimization Method (pages 463–477): Yeliang Zhang and Salim Hariri
Chapter 22 DistDLB: Improving Cosmology SAMR Simulations on Distributed Computing Systems Through Hierarchical Load Balancing (pages 479–501): Zhiling Lan, Valerie E. Taylor and Yawei Li