Structural Prediction Models for High-Performance Distributed Applications
Jennifer M. Schopf · 1997
We present a structural performance model to predict an application's performance on a set of distributed resources. We decompose application performance in accordance with the structure of the application, that is, into interacting component models that correspond to component sub-tasks. Then, using the application profile and available information as guides, we select models for each component appropriately. This allows different modeling approaches for different application components as needed. As a proof-of-concept, we have implemented this approach for two distributed applications, a master-slave genetic algorithm code and a red-black stencil successive over-relaxation code. We achieve predictions within 10% while demonstrating the flexibility this framework allows. 1 Introduction Clusters of distributed machines have become a common platform for high performance applications but remain a challenging environment in which to achieve good performance. One reason for this is the di...