We plan to develop a programming abstraction to enable programmers to write efficient parallel programs to process dynamic graphs.

Many graph processing frameworks have been developed for computing on static graphs, but graphs are often changing very quickly. It is therefore important to be able to incorporate graph updates into computations without restarting the computations from scratch. This project aims to design a high-level programming framework to enable users to more easily write efficient parallel graph codes on dynamic graphs. The project will involve studying and improving upon existing parallel algorithms on dynamic graphs, and identifying common operations among the algorithms that can be incorporated into a programming abstraction.