Towards Scalable Identification of Motifs Representing Non-Determinism in HPC Simulations
DescriptionWe present a novel parallel framework for large scale network alignment. Network alignment has applications in many disciplines including bioinformatics and social sciences. Our algorithm is one of the first network alignment tools that can not only identify similar networks, but also identify the differences between nearly similar networks. It is particularly useful in finding regions of non-determinism in event graphs, arising in large HPC simulations.
Our algorithm compares similarity between vertices based the number of graphlets (or motifs) to which the vertex belongs. Thus, it can also be used to find motifs in a graph. However, compared to the state-of-the art algorithms, our algorithm can (i) compute multiple motifs in one execution and (ii) be tuned to graph structure and user specification. We will present the algorithm, showcase the scalability results, and compare its performance and accuracy with other state-of-the art software.
Our algorithm compares similarity between vertices based the number of graphlets (or motifs) to which the vertex belongs. Thus, it can also be used to find motifs in a graph. However, compared to the state-of-the art algorithms, our algorithm can (i) compute multiple motifs in one execution and (ii) be tuned to graph structure and user specification. We will present the algorithm, showcase the scalability results, and compare its performance and accuracy with other state-of-the art software.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Posters
TimeTuesday, 15 November 20228:30am - 5pm CST
LocationC1-2-3
TP
XO/EX
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