Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications (Wiley Series on Parallel and Distributed Computing Series) / Edition 1

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Overview

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.

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Editorial Reviews

From the Publisher
"This edited volume brings together a high-powered list of experts mostly from leading research instates and universities in the US to deal, with the various aspects of parallel and distributed computing. It shall be valued greatly all over the world. Written in a reasoned and intelligible manner, it shall have an assured place in the parallel and distributed computing library where it should be accessible to any reader with a solid background in the subject." (Current Engineering Practice, 1 November 2010)
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Product Details

Meet the Author

Manish Parashar, PhD, is Professor of Electrical and Computer Engineering at Rutgers University, where he is also the director of the Applied Software Systems Laboratory and director of the NSF Center for Autonomic Computing. He has received numerous awards, including the Rutgers Board of Trustees Award for Excellence in Research (2004-2005) and the NSF CAREER Award (1999).

Xiaolin Li, PhD, is Assistant Professor of Computer Science at Oklahoma State University.

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Table of Contents

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 Bozdağ, 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.

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