Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications / Wiley Series on Parallel and Distributed Computing (PDF)
(Sprache: Englisch)
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...
architectures, and implementations of advanced computational
infrastructures and the applications they support
Emerging large-scale adaptive scientific and engineering
applications are...
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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.
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.
Inhaltsverzeichnis zu „Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications / Wiley Series on Parallel and Distributed Computing (PDF)“
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).
... mehr
Part III Dynamic Partitioning and Adaptive Runtime Management Frameworks. 15. Hypergraph-Based Dynamic Partitioning and Load Balancing (Umit V. Catalyurek, Doruk Bozda& #287;, 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.
... weniger
Autoren-Porträt von Manish Parashar, Xiaolin Li, Sumir Chandra
Manish Parashar, PhD, is Professor of Electrical andComputer 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.
Bibliographische Angaben
- Autoren: Manish Parashar , Xiaolin Li , Sumir Chandra
- 2010, 1. Auflage, 544 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 0470558016
- ISBN-13: 9780470558010
- Erscheinungsdatum: 12.01.2010
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