Rent 9780470072943 | Advanced Computational Infrastructures for Parallel and Distributed Applications | Parashar, Manish; Li, Xiaolin; Chandra, Sumir @knetbooks.com

Summary Table of Contents Author Biography

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 u... MOREseful for practitioners and students in these fields.

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.

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

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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