A highly parallel algorithm to approximate MaxCut on distributed memory architectures

We develop and experiment with a new parallel algorithm to approximate the maximum weight cut in a weighted undirected graph. Our implementation is based on the recent new algorithm of Goemans and Williamson for this problem. However, our work aims for an efficient, practical formulation of the algorithm with close to optimal parallelization. Our theoretical analysis and an implementation on the Connection Machine CM5 show that our parallelization achieves linear speedup. We test our implementation on several large graphs (more than 9000 vertices), particularly on large instances of the Ising model