Operating the electrical power grid to prevent power black-outs is a complex task. An important aspect of this is contingency analysis, which involves understanding and mitigating potential failures in power grid elements such as transmission lines. When taking into account the potential for multiple simultaneous failures (known as the N-x contingency problem), contingency analysis becomes a massively computational task. In this paper we describe a novel hybrid computational approach to contingency analysis. This approach exploits the unique graph processing performance of the Cray XMT in conjunction with a conventional massively parallel compute cluster to identify likely simultaneous failures that could cause widespread cascading power failures that have massive economic and social impact on society. The approach has the potential to provide the first practical and scalable solution to the N-x contingency problem. When deployed in power grid operations, it will increase the grid operator’s ability to deal effectively with outages and failures with power grid components while preserving stable and safe operation of the grid. The paper describes the architecture of our solution and presents preliminary performance results that validate the efficacy of our approach.