With the rapid change of computing architectures, and variety of programming models; the ability to develop performance portable applications has become of great importance. This is particularly true in large production codes where developing and maintaining hardware specific versions is untenable.

We discuss parallel performance of structured adaptive mesh refinement calculations using the SAMRAI library. We focus on fundamental aspects of adaptive gridding and dynamic computation of changing data dependencies. Previous analysis of performance of large-scale parallel adaptive calculations revealed poor scaling in these operations. Specifically, we found that these operations are inexpensive for small problems, but that their costs can become unacceptable for problems run on large numbers of processors. This paper describes subsequent developments involving graph- and tree-based algorithms that reduce runtime complexity and substantially increase scalability. We characterize performance on realistic adaptive problems using up to 512 processors of an IBM SP system and up to 1024 processors of a Linux cluster.

Abstract A major challenge facing software libraries for scientific computing is the ability to provide adequate flexibility to meet sophisticated, diverse, and evolving application requirements. Object‐oriented design techniques are valuable tools for capturing characteristics of complex applications in a software architecture. In this paper, we describe certain prominent object‐oriented features of the SAMRAI software library that have proven to be useful in application development. SAMRAI is used in a variety of applications and has demonstrated a substantial amount of code and design re‐use in those applications. This flexibility and extensibility is illustrated with three different application codes. We emphasize two important features of our design. First, we describe the composition of complex numerical algorithms from smaller components which are usable in different applications. Second, we discuss the extension of existing framework components to satisfy new application needs. Published in 2002 by John Wiley & Sons, Ltd.

This paper discusses the design and performance of the parallel data communication infrastructure in SAMRAI, a software framework for structured adaptive mesh refinement (SAMR) multi-physics applications. We describe requirements of such applications and how SAMRAI abstractions manage complex data communication operations found in them. Parallel performance is characterized for two adaptive problems solving hyperbolic conservation laws on up to 512 processors of the IBM ASCI Blue Pacific system. Results reveal good scaling for numerical and data communication operations but poorer scaling in adaptive meshing and communication schedule construction phases of the calculations. We analyze the costs of these different operations, addressing key concerns for scaling SAMR computations to large numbers of processors, and discuss potential changes to improve our current implementation.