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Future and current high-performance computing applications will have to change and adapt as node architectures evolve. The application of advanced architecture simulators will play a crucial role for the design and optimization of future data intensive applications. In this paper, we present our simulation-based framework for analyzing the scalability and performance of a number of critical optimizations of a massively parallel genomic search application, mpiBLAST, using an advanced macroscale simulator (SST/macro). We report the use of our framework for the evaluation of three potential improvements of mpiBLAST: 1) enabling high-performance parallel output; 2) an approach for caching database fragments in memory; and 3) a methodology for pre-distributing database segments. In our experimental setup, we performed query sequence matching on the genome of the yellow fever mosquito, Aedes aegypti.