A Comparative Study on Exact Triangle Counting Algorithms on the GPU

TL;DR

This paper compares three GPU-based algorithms for exact triangle counting in graphs, demonstrating superior performance over CPU methods and highlighting the graph-analytic approach's efficiency due to filtering and set-intersection techniques.

Contribution

It introduces and evaluates three distinct GPU algorithms for triangle counting, showing the graph-analytic method's superior performance and practical advantages.

Findings

All three GPU algorithms outperform CPU implementations.

The graph-analytic approach achieves the best performance.

Filtering and set-intersection are key to efficiency.

Abstract

We implement exact triangle counting in graphs on the GPU using three different methodologies: subgraph matching to a triangle pattern; programmable graph analytics, with a set-intersection approach; and a matrix formulation based on sparse matrix-matrix multiplies. All three deliver best-of-class performance over CPU implementations and over comparable GPU implementations, with the graph-analytic approach achieving the best performance due to its ability to exploit efficient filtering steps to remove unnecessary work and its high-performance set-intersection core.