Predictive performance and scalability modeling of a large-scale application

• Published in: ACM/IEEE SC 2001 Conference (SC'01) p. 37
• Main Authors: Kerbyson, D. J., Alme, H. J., Hoisie, A., Petrini, F., Wasserman, H. J., Gittings, M.
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 10-11-2001; IEEE
• Series: ACM Conferences
• Subjects: Adaptive mesh refinement; Computer architecture; Computer systems organization > Architectures > Distributed architectures > Grid computing; Computer systems organization > Architectures > Parallel architectures > Multicore architectures; Computer systems organization > Architectures > Serial architectures > Superscalar architectures; Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies; Computing methodologies > Modeling and simulation > Simulation types and techniques > Massively parallel and high-performance simulations; full application codes; Government; Hydrodynamics; Laboratories; Large-scale systems; Multidimensional systems; parallel system architecture; Performance analysis; Predictive models; Scalability; Software and its engineering > Software organization and properties > Software system structures > Distributed systems organizing principles > Grid computing; Software and its engineering > Software organization and properties > Software system structures > Distributed systems organizing principles > Organizing principles for web applications; Teraflop scale computing; full application codes; Teraflop scale computing; parallel system architecture; Performance analysis
• ISBN: 9781581132939; 158113293X
• DOI: 10.1145/582034.582071

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.