SCoOL - Scalable Common Optimization Library

We propose SCoOL, a programming model and its corresponding parallel runtime systems for implementing optimization problem solvers. In SCoOL, users specify what task is performed for a point in a given search space, and what global information should be maintained during the search. The resulting optimization program is then efficiently executed in a BSP-style on a shared or distributed memory computers by a parallel runtime provided with the model. In the paper, we show details of our scalable runtime for distributed memory clusters, including algorithms for work stealing and tasks rebalancing. To benchmark the platform, we implement solutions to several optimization problems and provide performance analysis for Quadratic Assignment Problem, Parent Set Assignment, and Bayesian Networks Structure Learning. Our solvers show strong scaling on a cluster with 1,280 cores, significantly outperforming the current state-of-the-art solvers in Bayesian networks learning.