We present a decomposition strategy to speed up constraint optimization for a representative multiprocessor scheduling problem. In the manner of Benders decomposition, our technique solves relaxed versions of the problem and iteratively learns constraints to prune the solution space. Typical formulations suffer prohibitive run times even on medium-sized problems with less than 30 tasks. Our decomposition strategy enhances constraint optimization to robustly handle instances with over 100 tasks. Moreover, the extensibility of constraint formulations permits realistic application and resource constraints, which is a limitation of common heuristic methods for scheduling. The inherent extensibility, coupled with improved run times from a decomposition strategy, posit constraint optimization as a powerful tool for resource constrained scheduling and multiprocessor design space exploration. 1
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