An efficient GPU implementation of the revised simplex method

The computational power provided by the massive parallelism of modern graphics processing units (GPUs) has moved increasingly into focus over the past few years. In particular, general purpose computing on GPUs (GPGPU) is attracting attention among researchers and practitioners alike. Yet GPGPU research is still in its infancy, and a major challenge is to rearrange existing algorithms so as to obtain a significant performance gain from the execution on a GPU. In this paper, we address this challenge by presenting an efficient GPU implementation of a very popular algorithm for linear programming, the revised simplex method. We describe how to carry out the steps of the revised simplex method to take full advantage of the parallel processing capabilities of a GPU. Our experiments demonstrate considerable speedup over a widely used CPU implementation, thus underlining the tremendous potential of GPGPU.