Cover image for Advanced computational infrastructures for parallel and distributed adaptive applications
Title:
Advanced computational infrastructures for parallel and distributed adaptive applications
Series:
Wiley series on parallel and distributed computing
Publication Information:
Hoboken, NJ : John Wiley & Sons, 2010
Physical Description:
xx, 518 p. : ill. ; 24 cm.
ISBN:
9780470072943

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30000010236596 QA76.58 A38 2010 Open Access Book Book
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Summary

Summary

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.


Author Notes

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.


Table of Contents

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