Grid-Enabled Optimization with GAMS

Abstract

We describe a framework for modeling optimization problems for solution on a grid computer. The framework is easy to adapt to multiple grid engines and can seamlessly integrate evolving mechanisms from particular computing platforms. It facilitates the widely used master-worker model of computing and is shown to be flexible and powerful enough for a large variety of optimization applications. In particular, we summarize a number of new features of the GAMS modeling system that provide a lightweight, portable, and powerful framework for optimization on a grid. We provide downloadable examples of its use for embarrasingly parallel financial applications, decomposition of complementarity problems, and for solving very difficult mixed-integer programs to optimality. Computational results are provided for a number of different grid engines, including multicore machines, a pool of machines controlled by the Condor resource manager, and the grid engine from Sun Microsystems.