MULTI-CORE PARALLEL GRAPH ALGORITHMS

Abstract

Large sizes of real-world data graphs, such as social networks, communication networks, hyperlink networks, and model-checking networks, call for fast and scalable analytic algorithms. The shared-memory multicore machine is a prevalent parallel computation model that can handle such volumes of data. Unfortunately, many graph algorithms do not take full advantage of such a parallel model. This thesis focuses on the parallelism of two graph problems, graph trimming and core maintenance. Graph trimming is to prune the vertices without outgoing edges; core maintenance is to maintain the core numbers of vertices when inserting or removing edges, where the core number of a vertex can be a parameter of density in the graph. The goal of this thesis is to develop fast, provable, and scalable parallel graph algorithms that perform on shared-memory multicore machines. Toward this goal, we first discuss the sequential algorithms and then propose corresponding parallel algorithms. The thesis adopts a three-pronged approach of studying parallel graph algorithms from the algorithm design, correctness proof, and performance analysis. Our experiments on multicore machines show significant speedups over various real and synthetic graphs.