Advanced computational infrastructures for parallel and distributed adaptive applications / edited by Manish Parashar, Xiaolin Li.

ABOUT THE AUTHOR
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.

Includes bibliographical references and index.

TABLE OF CONTENTS
Preface xi

Contributors xv

Biographies xix

1. Introduction: Enabling Large-Scale Computational Science—Motivations, Requirements, and
Challenges 1
Manish Parashar and Xiaolin Li

Part I Adaptive Applications in Science and Engineering

2. Adaptive Mesh Refinement MHD Simulations of Tokamak Refueling 11
Ravi Samtaney

3. Parallel Computing Engines for Subsurface Imaging Technologies 29
Tian-Chyi J. Yeh, Xing Cai, Hans P. Langtangen, Junfeng Zhu, and Chuen-Fa Ni

4. PlaneWave Seismic Data: Parallel and Adaptive Strategies for Velocity Analysis and Imaging 45
Paul L. Stoffa, Mrinal K. Sen, Roustam K. Seif, and Reynam C. Pestana

5. Data-Directed Importance Sampling for Climate Model Parameter Uncertainty Estimation 65
Charles S. Jackson, Mrinal K. Sen, Paul L. Stoffa, and Gabriel Huerta

6. Adaptive Cartesian Methods for Modeling Airborne Dispersion 79
Andrew Wissink, Branko Kosovic, Marsha Berger, Kyle Chand, and Fotini K. Chow

7. Parallel and Adaptive Simulation of Cardiac Fluid Dynamics 105
Boyce E. Griffith, Richard D. Hornung, David M. McQueen, and Charles S. Peskin

8. Quantum Chromodynamics on the BlueGene/L Supercomputer 131
Pavlos M. Vranas and Gyan Bhanot

Part II Adaptive Computational Infrastructures

9. The SCIJump Framework for Parallel and Distributed Scientific Computing 151
Steven G. Parker, Kostadin Damevski, Ayla Khan, Ashwin Swaminathan, and Christopher R. Johnson

10. Adaptive Computations in the Uintah Framework 171
Justin Luitjens, James Guilkey, Todd Harman, Bryan Worthen, and Steven G. Parker

11. Managing Complexity in Massively Parallel, Adaptive, Multiphysics Finite Element Applications 201
Harold C. Edwards

12. GrACE: Grid Adaptive Computational Engine for Parallel Structured AMR Applications 249
Manish Parashar and Xiaolin Li

13. Charm++ and AMPI: Adaptive Runtime Strategies via Migratable Objects 265
Laxmikant V. Kale and Gengbin Zheng

14. The Seine Data Coupling Framework for Parallel Scientific Applications 283
Li Zhang, Ciprian Docan, and Manish Parashar

Part III Dynamic Partitioning and Adaptive Runtime Management Frameworks

15. Hypergraph-Based Dynamic Partitioning and Load Balancing 313
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 335
Jian Chen and Valerie E. Taylor

17. Variable Partition Inertia: Graph Repartitioning and Load Balancing for Adaptive Meshes 357
Chris Walshaw

18. A Hybrid and Flexible Data Partitioner for Parallel SAMR 381
Johan Steensland

19. Flexible Distributed Mesh Data Structure for Parallel Adaptive Analysis 407
Mark S. Shephard and Seegyoung Seol

20. HRMS: Hybrid Runtime Management Strategies for Large-Scale Parallel Adaptive Applications 437
Xiaolin Li and Manish Parashar

21. Physics-Aware Optimization Method 463
Yeliang Zhang and Salim Hariri

22. DistDLB: Improving Cosmology SAMR Simulations on Distributed Computing Systems Through Hierarchical Load Balancing 479
Zhiling Lan, Valerie E. Taylor, and Yawei Li

Index 503

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.