Parallel Clustering Algorithms for Structured AMR

We compare several different parallel implementation approaches for the clustering operations performed during adaptive gridding operations in patch-based structured adaptive mesh refinement (SAMR) applications. Specifically, we target the clustering algorithm of Berger and Rigoutsos (BR91), which is commonly used in many SAMR applications. The baseline for comparison is a simplistic parallel extension of the original algorithm that works well for up to O(10{sup 2}) processors. Our goal is a clustering algorithm for machines of up to O(10{sup 5}) processors, such as the 64K-processor IBM BlueGene/Light system. We first present an algorithm that avoids the unneeded communications of the simplistic approach to improve the clustering speed by up to an order of magnitude. We then present a new task-parallel implementation to further reduce communication wait time, adding another order of magnitude of improvement. The new algorithms also exhibit more favorable scaling behavior for our test problems. Performance is evaluated on a number of large scale parallel computer systems, including a 16K-processor BlueGene/Light system

Irregular Coarse-Grain Data Parallelism Under LPARX

LPARX is a software development tool for implementing dynamic, irregular scientific applications, such as multilevel multilevel finite difference methods and particle methods, on high performance MIMD parallel architectures. It supports coarse grain data parallelism and gives the application complete control over specifying arbitrary block decompositions. LPARX provides structural abstraction, representing data decompositions as first-class objects that can be manipulated and modified at run-time. LPARX, implemented as a C++ class library, is currently running on diverse MIMD platforms, including the Intel Paragon, Cray C-90, IBM SP2, and networks of workstations running under PVM. Software may be developed and debugged on a single processor workstation. 1 Introduction An outstanding problem in scientific computation is how to manage the complexity of converting mathematical descriptions of dynamic, irregular numerical algorithms into high performance applications software. Non-unifo..