Summary form only given. The Los Alamos Crestone Project is part of the Department of Energy's (DOE) Accelerated Strategic Computing Initiative, or ASCI Program. The main goal of this software development project is to investigate the use of continuous adaptive mesh refinement (CAMR) techniques for application to problems of interest to the Laboratory. There are many code development efforts in the Crestone Project, both unclassified and classified codes. An overview of the Crestone Project, and the SAGE and RAGE codes, has been published recently in Weaver and Gittings (2003). In This work, I will give the status of the use of these CAMR codes on commodity cluster machines. One of the most economical methods for achieving supercomputing capability is to use commodity processors connected by commodity interconnects. This was highlighted recently at Virginia Tech when Dr. Varadarajan built the third fastest supercomputer in the world by connecting 1100 dual-processor Macintosh G5 machines together (see http://www.top500.org). Most commodity clusters use a form of LINUX as the operating system. We will give an overview of the current status of using the Crestone Project codes SAGE and RAGE on commodity cluster machines. These codes are intended for general applications without tuning of algorithms or parameters. We have run a wide variety of physical applications from millimeter-scale laboratory laser experiments, to the multikilometer-scale asteroid impacts into the Pacific Ocean, to parsec-scale galaxy formation. Examples of these simulations will be shown. The goal of our effort is to avoid ad hoc models and attempt to rely on first-principles physics. In addition to the large effort on developing parallel code physics packages, a substantial effort in the project is devoted to improving the computer science and software quality engineering (SQE) of the Project codes as well as a sizable effort on the verification and validation (V&V) of the resulting codes. Examples of these efforts for our project will be discussed. Recent results of the scaling of these codes on commodity clusters will be shown.

In this work we present a predictive analytical model that encompasses the performance and scaling characteristics of an important ASCI application. SAGE (SAIC's Adaptive Grid Eulerian hydrocode) is a multidimensional hydrodynamics code with adaptive mesh refinement. The model is validated against measurements on several systems including ASCI Blue Mountain, ASCI White, and a Compaq Alphaserver ES45 system showing high accuracy. It is parametric --- basic machine performance numbers (latency, MFLOPS rate, bandwidth) and application characteristics (problem size, decomposition method, etc.) serve as input. The model is applied to add insight into the performance of current systems, to reveal bottlenecks, and to illustrate where tuning efforts can be effective. We also use the model to predict performance on future systems.