The rise of graph analytic systems has created a need for new ways to measure
and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE
Graph Challenge has been developed to provide a well-defined community venue
for stimulating research and highlighting innovations in graph analysis
software, hardware, algorithms, and systems. GraphChallenge.org provides a wide
range of pre-parsed graph data sets, graph generators, mathematically defined
graph algorithms, example serial implementations in a variety of languages, and
specific metrics for measuring performance. Graph Challenge 2017 received 22
submissions by 111 authors from 36 organizations. The submissions highlighted
graph analytic innovations in hardware, software, algorithms, systems, and
visualization. These submissions produced many comparable performance
measurements that can be used for assessing the current state of the art of the
field. There were numerous submissions that implemented the triangle counting
challenge and resulted in over 350 distinct measurements. Analysis of these
submissions show that their execution time is a strong function of the number
of edges in the graph, Ne​, and is typically proportional to Ne4/3​ for
large values of Ne​. Combining the model fits of the submissions presents a
picture of the current state of the art of graph analysis, which is typically
108 edges processed per second for graphs with 108 edges. These results
are 30 times faster than serial implementations commonly used by many graph
analysts and underscore the importance of making these performance benefits
available to the broader community. Graph Challenge provides a clear picture of
current graph analysis systems and underscores the need for new innovations to
achieve high performance on very large graphs.Comment: 7 pages, 6 figures; submitted to IEEE HPEC Graph Challenge. arXiv
admin note: text overlap with arXiv:1708.0686