Exploring optimizations on shared-memory platforms for parallel triangle counting algorithms

The widespread use of graphs to model large scale real-world data brings with it the need for fast graph analytics. In this paper, we explore the problem of triangle counting, a fundamental graph-analytic operation, on shared-memory platforms. Existing triangle counting implementations do not effectively utilize the key characteristics of large sparse graphs for tuning their algorithms for performance. We explore such optimizations and develop faster serial and parallel variants of existing algorithms, which outperform the state-of-the-art on Intel manycore and multicore processors. Our algorithms achieve good strong scaling on many graphs with varying scale and degree distributions. Furthermore, we extend our optimizations to a well-known graph processing framework, GraphMat, and demonstrate their generality.