The digital world has given rise to massive quantities of data that include rich semantic and complex networks. A social
graph, for example, containing hundreds of millions of actors and tens of billions of relationships is not uncommon. Analyzing
these large datasets, even to answer simple analytic queries, often pushes the limits of algorithms and machine architectures.
We present GraphCT, a scalable framework for graph analysis using parallel and multithreaded algorithms on shared memory
platforms. Utilizing the unique characteristics of the Cray XMT, GraphCT enables fast network analysis at unprecedented scales
on a variety of input datasets. On a synthetic power law graph with 2 billion vertices and 17 billion edges, we can find the
connected components in under 2 minutes. We can estimate the betweenness centrality of a similar graph with 537 million
vertices and over 8 billion edges in under one hour. GraphCT is built for portability and performance.