KTRussExPLORER: Exploring the Design Space of K-truss Decomposition Optimizations on GPUs

摘要

K-truss decomposition is an important method in graph analytics for finding cohesive subgraphs in a graph. Various works have accelerated k-truss decomposition on GPUs and have proposed different optimizations while doing so. The combinations of these optimizations form a large design space. However, most GPU implementations focus on a specific combination or set of combinations in this space. This paper surveys the optimizations applied to k-truss decomposition on GPUs, and presents KTRussExPLORER, a framework for exploring the design space formed by the combinations of these optimizations. Our evaluation shows that the best combination highly depends on the graph of choice, and analyses the conditions that make each optimization attractive. Some of the best combinations we find outperform previous Graph Challenge champions on many large graphs.