Year: 2010
Type: preprint
Abstract: We propose a new graph-theoretic benchmark in this paper. The benchmark is developed to address shortcomings of an existing widely-used graph benchmark. We thoroughly studied a large number of traditional and contemporary graph algorithms reported in the literature to have clear understanding of their algorithmic and run-time characteristics. Based on this study, we designed a suite of kernels, each of which represents a specific class of graph algorithms. The kernels are designed to capture the typical run-time behavior of target algorithms accurately, while limiting computational and spatial overhead to ensure its computation finishes in reasonable time. We expect that the developed benchmark will serve as a much needed tool for evaluating different architectures and programming models to run graph algorithms.
Source: arXiv (Cornell University)
Authors: Yoo, Andy B., Liu, Yang, Vaidya, Sheila, Poole, Stephen
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1593308684