Abstract
List intersections are ubiquitous and can be found in a wide range of applications, including triangle counting and finding the maximal k-truss, both of which are part of the HPEC Static Graph Challenge. For many graph based problems it is necessary to find intersections for a very large number of lists-these lists tend to vary greatly in size and are difficult to efficiently load-balance. Numerous parallel algorithms on list intersections for triangle counting have been proposed, but load-balancing decisions are typically made at a global level. In this paper we present an efficient and adaptive approach to load-balancing at a finer granularity. Our approach assigns a different number of threads for different intersections in order to effectively utilize the resources of the GPU. We show the applicability of our load-balancing method to two different intersection methods, one search-based and one merge-based. Our algorithm outperforms several recent triangle counting algorithms, including recent HPEC Graph Challenge Champions.
Oded Green
Senior Solutions Architect
David A. Bader
Distinguished Professor, Associate Dean for Research, and Director of the Institute for Data Science
David A. Bader is a Distinguished Professor in the Department of Data Science and Associate Dean for Research in the Ying Wu College of Computing at New Jersey Institute of Technology.