Enhancing adaptive middleware for quantum chemistry applications with a database framework

Quantum chemistry applications such as the General Atomic and Molecular Electronic Structure System (GAMESS) that can execute on a complex peta-scale parallel computing environment has a large number of input parameters that affect the overall performance. The application characteristics vary according to the input parameters. This is due to the difference in the usage of resources like network bandwidth, I/O and main memory, according to the input parameters. Effective execution of applications in a parallel computing environment that share such resources require some sort of adaptive mechanism to enable efficient usage of these resources. In our previous work, we have integrated GAMESS with an adaptive middleware NICAN (Network Information Conveyer and Application Notification) for dynamic adaptations during heavy load conditions that modify execution of GAMESS computations on a per-iteration basis. This leads to better application performance. In this research, we have expanded the structure of NICAN in order to include other input parameters based on which application performance can be controlled. The application performance has been analyzed on different architectures and a tuning strategy has been identified. A generic database framework has been incorporated in the existing NICAN mechanism so as to aid this tuning strategy.