Proceedings of the 9th annual conference on Genetic and evolutionary computation
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UltraScan gateway enhancements: in collaboration with TeraGrid advanced user support
Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
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We present a novel divide and conquer method for parallelizing a large scale multivariate linear optimization problem, which is commonly solved using a sequential algorithm with the entire parameter space as the input. The optimization solves a large parameter estimation problem where the result is sparse in the parameters. By partitioning the parameters and the associated computations, our technique overcomes memory constraints when used in the context of a single workstation and achieves high processor utilization when large workstation, clusters are used. We implemented this technique in a widely used software package for the analysis of a biophysics problem, which is representative for a large class of problems in the physical sciences. We evaluate the performance of the proposed method on a 512-processor cluster and offer an analytical model for predicting the performance of the algorithm.