Parashar / Li / Chandra Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications

1. Auflage 2010
ISBN: 978-0-470-55801-0
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 544 Seiten, E-Book

Reihe: Wiley Series on Parallel and Distributed Computing

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

Emerging 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 systems

The cross-disciplinary approach of this reference delivers acomprehensive discussion of the requirements, design challenges,underlying 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.

Autoren/Hrsg.

Parashar, Manish

Li, Xiaolin

Chandra, Sumir

Weitere Mitwirkende

Zomaya, Albert Y.

Inhaltsverzeichnis

Preface xi
Contributors xv
Biographies xix
1. Introduction: Enabling Large-Scale Computational Science--Motivations, Requirements, and
Challenges 1
Manish Parashar and Xiaolin Li
Part I Adaptive Applications in Science and Engineering
2. Adaptive Mesh Refinement MHD Simulations of Tokamak Refueling 11
Ravi Samtaney
3. Parallel Computing Engines for Subsurface Imaging Technologies 29
Tian-Chyi J. Yeh, Xing Cai, Hans P. Langtangen, Junfeng Zhu, and Chuen-Fa Ni
4. PlaneWave Seismic Data: Parallel and Adaptive Strategies for Velocity Analysis and Imaging 45
Paul L. Stoffa, Mrinal K. Sen, Roustam K. Seif, and Reynam C. Pestana
5. Data-Directed Importance Sampling for Climate Model Parameter Uncertainty Estimation 65
Charles S. Jackson, Mrinal K. Sen, Paul L. Stoffa, and Gabriel Huerta
6. Adaptive Cartesian Methods for Modeling Airborne Dispersion 79
Andrew Wissink, Branko Kosovic, Marsha Berger, Kyle Chand, and Fotini K. Chow
7. Parallel and Adaptive Simulation of Cardiac Fluid Dynamics 105
Boyce E. Griffith, Richard D. Hornung, David M. McQueen, and Charles S. Peskin
8. Quantum Chromodynamics on the BlueGene/L Supercomputer 131
Pavlos M. Vranas and Gyan Bhanot
Part II Adaptive Computational Infrastructures
9. The SCIJump Framework for Parallel and Distributed Scientific Computing 151
Steven G. Parker, Kostadin Damevski, Ayla Khan, Ashwin Swaminathan, and Christopher R. Johnson
10. Adaptive Computations in the Uintah Framework 171
Justin Luitjens, James Guilkey, Todd Harman, Bryan Worthen, and Steven G. Parker
11. Managing Complexity in Massively Parallel, Adaptive, Multiphysics Finite Element Applications 201
Harold C. Edwards
12. GrACE: Grid Adaptive Computational Engine for Parallel Structured AMR Applications 249
Manish Parashar and Xiaolin Li
13. Charm++ and AMPI: Adaptive Runtime Strategies via Migratable Objects 265
Laxmikant V. Kale and Gengbin Zheng
14. The Seine Data Coupling Framework for Parallel Scientific Applications 283
Li Zhang, Ciprian Docan, and Manish Parashar
Part III Dynamic Partitioning and Adaptive Runtime Management Frameworks
15. Hypergraph-Based Dynamic Partitioning and Load Balancing 313
Umit V. Catalyurek, Doruk Bozda?g, Erik G. Boman, Karen D. Devine, Robert Heaphy, and Lee A. Riesen
16. Mesh Partitioning for Efficient Use of Distributed Systems 335
Jian Chen and Valerie E. Taylor
17. Variable Partition Inertia: Graph Repartitioning and Load Balancing for Adaptive Meshes 357
Chris Walshaw
18. A Hybrid and Flexible Data Partitioner for Parallel SAMR 381
Johan Steensland
19. Flexible Distributed Mesh Data Structure for Parallel Adaptive Analysis 407
Mark S. Shephard and Seegyoung Seol
20. HRMS: Hybrid Runtime Management Strategies for Large-Scale Parallel Adaptive Applications 437
Xiaolin Li and Manish Parashar
21. Physics-Aware Optimization Method 463
Yeliang Zhang and Salim Hariri
22. DistDLB: Improving Cosmology SAMR Simulations on Distributed Computing Systems Through Hierarchical Load Balancing 479
Zhiling Lan, Valerie E. Taylor, and Yawei Li
Index 503

Über Autor(innen)

Manish Parashar, PhD, is Professor of Electrical andComputer Engineering at Rutgers University, where he is also thedirector of the Applied Software Systems Laboratory and director ofthe NSF Center for Autonomic Computing. He has received numerousawards, including the Rutgers Board of Trustees Award forExcellence in Research (2004-2005) and the NSF CAREER Award (1999).
Xiaolin Li, PhD, is Assistant Professor of ComputerScience at Oklahoma State University.