Cross-document coreference, the task of grouping all the mentions of each entity in a document collection, arises in information extraction and automated knowledge base construction. For large collections, it is clearly impractical to consider all possible groupings of mentions into distinct entities. To solve the problem we propose two ideas: (a) a distributed inference technique that uses parallelism to enable large scale processing, and (b) a hierarchical model of coreference that represents uncertainty over multiple granularities of entities to facilitate more effective approximate inference. To evaluate these ideas, we constructed a labeled corpus with 1.5 million mentions from links to Wikipedia entities available on the web. We show that the combination of the hierarchical model and distributed inference quickly obtains high accuracy (38% error reduction) on this large dataset, demonstrating the scalability of our approach.