A unique investigation of the state of the art in design, architectures, and implementations of advanced computational infrastructures and the applications they supportEmerging 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.
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.
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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.
Arvustused
"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)
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.
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.
