Computing large sparse multivariate optimization problems with an application in biophysics

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.