A performance prediction framework for scientific applications

This work presents the results of ongoing investigations in the development of a performance modeling framework, developed by the Performance Modeling and Characterization (PMaC) Lab at the San Diego Supercomputer Center. The framework is faster than traditional cycle-accurate simulation, more sophisticated than performance estimation based on system peak-performance metrics, and is shown to be effective on benchmarks and scientific applications. This paper focuses on one such functionality by investigating sensitivity studies to further understand observed and anticipated effect of both the architecture and the application in predicted runtime.