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FuseIM: Fusing Probabilistic Traversals for Influence Maximization on Exascale Systems

Published: 03 June 2024 Publication History

Abstract

Probabilistic breadth-first traversals (BPTs) are used in many network science and graph machine learning applications. In this paper, we are motivated by the application of BPTs in stochastic diffusion-based graph problems such as influence maximization. These applications heavily rely on BPTs to implement a Monte-Carlo sampling step for their approximations. Given the large sampling complexity, stochasticity of the diffusion process, and the inherent irregularity in real-world graph topologies, efficiently parallelizing these BPTs remains significantly challenging. In this paper, we present a new algorithm to fuse a massive number of concurrently executing BPTs with random starts on the input graph. Our algorithm is designed to fuse BPTs by combining separate probabilistic traversals into a unified frontier. To show the general applicability of the fused BPT technique, we have incorporated it into two state-of-the-art influence maximization parallel implementations (gIM and Ripples). Our experiments on up to 4K nodes of the OLCF Frontier supercomputer (32,768 GPUs and 196K CPU cores) show strong scaling behavior, and that fused BPTs can improve the performance of these implementations up to 182.13× (avg. 75.15×) and 359.86× (avg. 135.17×) for gIM and Ripples, respectively.

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cover image ACM Conferences
ICS '24: Proceedings of the 38th ACM International Conference on Supercomputing
May 2024
582 pages
ISBN:9798400706103
DOI:10.1145/3650200
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 03 June 2024

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  1. Influence Maximization
  2. Parallel Graph Algorithms

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