Advanced Computational Infrastructures for l and Distributed Adaptive Applications
Published on 10. December 2009
Software
Other digital
544 pages
978-0-470-55802-7 (ISBN)
Description
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 useful for practitioners and students in these fields.
Reviews / Votes
"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)More details
Language
English
Place of publication
Hoboken
United Kingdom
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
ISBN-13
978-0-470-55802-7 (9780470558027)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Manish Parashar | Xiaolin Li | Sumir Chandra
Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications
E-Book
01/2010
Wiley
€156.99
Available for download
Persons
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.
Author
Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ
Department of Computer Science, Oklahoma State University
Department of Electrical and Computer Engineering, Bombay University; Center for Advanced Electronics Engineering, Bombay University
Series Editor
University of Western Australia
Content
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 Bozda&g, 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.