A Comparative Study on Exact Triangle Counting Algorithms on the GPU

04/18/2018
by   Leyuan Wang, et al.
0

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.

READ FULL TEXT
research
09/16/2020

Towards an Objective Metric for the Performance of Exact Triangle Count

The performance of graph algorithms is often measured in terms of the nu...
research
09/04/2019

Fast BFS-Based Triangle Counting on GPUs

In this paper, we propose a novel method to compute triangle counting on...
research
03/18/2020

GraphChallenge.org Triangle Counting Performance

The rise of graph analytic systems has created a need for new ways to me...
research
09/30/2021

Breaking the hegemony of the triangle method in clique detection

We consider the fundamental problem of detecting/counting copies of a fi...
research
09/25/2020

A Block-Based Triangle Counting Algorithm on Heterogeneous Environments

Triangle counting is a fundamental building block in graph algorithms. I...
research
12/20/2020

IntersectX: An Efficient Accelerator for Graph Mining

Graph pattern mining applications try to find all embeddings that match ...
research
03/14/2021

TRUST: Triangle Counting Reloaded on GPUs

Triangle counting is a building block for a wide range of graph applicat...

Please sign up or login with your details

Forgot password? Click here to reset