USE OF PREDICTIVE PERFORMANCE MODELING DURING LARGE-SCALE SYSTEM INSTALLATION

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Abstract
In this paper we describe an important use of predictive application performance modeling - the validation of measured performance during a new large-scale system installation. Using a previously-developed and validated performance model for SAGE, a multidimensional, 3D, multi-material hydrodynamics code with adaptive mesh refinement, we were able to help guide the stabilization of the first phase of the Los Alamos ASCI Q supercomputer. We review the salient features of an analytical model for this code that has been applied to predict its performance on a large class of Tera-scale parallel systems. We describe the methodology applied during system installation and upgrades to establish a baseline for the achievable "real" performance of the system. We also show the effect on overall application performance of certain key subsystems such as PCI bus speed and multi-rail networks. We show that utilization of predictive performance models is also a powerful system debugging tool.

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