Abstract
We report the results of testing the performance of a new, efficient, and highly general-purpose parallel optimization method, based upon simulated annealing. This optimization algorithm was applied to analyze the network of interacting genes that control embryonic development and other fundamental biological processes. We found several sets of algorithmic parameters that lead to optimal parallel efficiency for up to 100 processors on distributed-memory MIMD architectures. Our strategy contains two major elements. First, we monitor and pool performance statistics obtained simultaneously on all processors. Second, we mix states at intervals to ensure a Boltzmann distribution of energies. The central scientific issue is the inverse problem, the determination of the parameters of a set of nonlinear ordinary differential equations by minimizing the total error between the model behavior and experimental observations.
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