Performance improvements for computational sciences such as biology, physics, and chemistry are critically dependent on advances in multicore and manycore hardware. However, these emerging systems require substantial investment in software development time to migrate, optimize, and validate existing science models. The focus of our study is to examine the step--by-step process of adapting new and existing computational biology models to multicore and distributed memory architectures. We analyze

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... different strategies that may be more efficient in multicore vs. manycore environments. Our target application, Kingen, was developed to simulate AMPAR ion channel activity and to optimize kinetic model rate constants to biological data. Kingen uses a genetic algorithm to stochastically search parameter space to find global optima. As each individual in the population describes a rate constant parameter set in the kinetic model and the model is evaluated for each individual, there is significant computational complexity and parallelism in even a simple model run.