Structured Adaptive Mesh Refinement (SAMR) Grid Methods

1. Front Matter

   Pages i-xii

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2. Programming Complexity of SAMR Algorithms

   1. Systems Engineering for High Performance Computing Software: The HDDA/DAGH Infrastructure for Implementation of Parallel Structured Adaptive Mesh

      • Manish Parashar, James C. Browne

      Pages 1-18

   2. HAMR: The Hierarchical Adaptive Mesh Refinement System

      • Henry Neeman

      Pages 19-51

   3. AMR++: A Design for Parallel Object-Oriented Adaptive Mesh Refinement

      • Dan Quinlan

      Pages 53-58

   4. RSL: A Parallel Runtime System Library for Regional Atmospheric Models with Nesting

      • John G. Michalakes

      Pages 59-74

   5. Software Abstractions and Computational Issues in Parallel Structured Adaptive Mesh Methods for Electronic Structure Calculations

      • Scott Kohn, John Weare, M. Elizabeth Ong, Scott Baden

      Pages 75-95

   6. Structured Adaptive Mesh Refinement using High Performance Fortran

      • Piyush Mehrotra

      Pages 97-110

3. Applicability and Numerical Challenges of SAMR Methods

   1. The Dynamics of Localized Coherent Structures and the Role of Adaptive Software in Multiscale Modeling

      • Christopher R. Myers

      Pages 111-125

   2. Progress, Results, and Experiences in Developing an Adaptive Solver for Steady State Turbulent Reacting Flows in Industrial Boilers and Furnaces

      • M. Pernice, M. J. Bockelie, D. Swensen, P. J. Smith

      Pages 127-151

   3. Making Arbitrarily Small Black Holes: Experiences with AMR in Numerical Relativity

      • Matthew W. Choptuik

      Pages 153-163

   4. A Hybrid AMR Application for Cosmology and Astrophysics

      • Greg L. Bryan, Michael L. Norman

      Pages 165-170

4. Back Matter

   Pages 171-180

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Structured adaptive mesh refinement (SAMR) methods have matured over the past 20 years and are now the method of choice for certain difficult problems, such as compressible flow. SAMR presents difficult technical challenges, both in terms of the numerical techniques involved and the complexity of the programming effort, especially on parallel computers. In order to gain insight into managing these difficulties, much research effort has been directed at mesh generation, parallel computation, and improvements in accuracy, aimed primarily at refinement interfaces. A major stumbling block in this endeavor is that many of these techniques entail substantial amounts of problem specific detail. Standardization is highly unlikely, except within narrowly defined problem domains. The papers presented in this collection are based on talks given at the Workshop on Structured Adaptive Mesh Refinement Grid Methods, held at the Institute for Mathematics and its Applications, University of Minnesota, on March 12-13 1997. They describe research to improve the general understanding of the application of SAMR to practical problems; identify issues critical to efficient and effective implementation on high performance computers; stimulate the development of a community code repository for software including benchmarks to assist in the evaluation of software and compiler technologies. The ten chapters of this volume have been divided into two parts reflecting two major issues in the topic: (I) programming complexity of SAMR algorithms and (II) applicability and numerical challenges of SAMR methods. Part I presents three programming environments and two libraries that address the concerns of efficient execution and reduced software development times of SAMR applications. Part II describes an overview of applications that can benefit from SAMR methods, ranging from crack propagation and industrial boilers to

• Fortran
• algorithms
• complexity
• mathematics
• programming
• combinatorics

• Department of Computer Science and Engineering, University of California, La Jolla, USA

  Scott B. Baden

• Computer Science and Engineering, University of Notre Dame, Notre Dame, USA

  Nikos P. Chrisochoides

• Department of Computer Science, Indiana University, Bloomington, USA

  Dennis B. Gannon

• National Center for Supercomputing Applications; and Astronomy Department, University of Illinois at Urbana-Champaign, Urbana, USA

  Michael L. Norman

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