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cuAlign: Scalable Network Alignment on GPU Accelerators

Published: 12 November 2023 Publication History

Abstract

Given two graphs, the objective of network alignment is to find a one-to-one mapping of vertices in one graph (A) to vertices in the other (B), such that the number of overlaps is maximized. We say that edges (i, j) ∈ A and (i′, j′) ∈ B are overlapped if i is mapped to i′ and j is mapped to j′. Network alignment is an important optimization problem with several applications in bioinformatics, computer vision and ontology matching. Since it is an NP-hard problem, efficient heuristics and scalable implementations are necessary. However, a combination of combinatorial and algebraic kernels within the network alignment algorithm poses significant hurdles for parallelization. Further, load imbalance and irregular DRAM traffic limit achievable performance on GPUs. In this work, we introduce a novel framework (cuAlign) that combines intra-network proximity using node (vertex) embedding, sparsification for computational efficiency, and belief propagation (BP) and approximate weighted matching for alignment. We demonstrate qualitative improvements up to over state-of-the-art approaches. We provide a scalable implementation targeting modern GPU accelerators. Our novel approach identifies and exploits unique structural properties of the BP-based algorithm and employs code fusion to reduce data movement between different steps of the algorithm. Using a diverse set of inputs, we demonstrate up to 19 × speedup for belief propagation, 3 × speedup for approximate weighted matching, and 15 × total, relative to a state-of-the-art multi-threaded implementation.

Supplemental Material

MP4 File
Recording of "cuAlign: Scalable Network Alignment on GPU Accelerators" presentation at IA3 2023.

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cover image ACM Other conferences
SC-W '23: Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis
November 2023
2180 pages
ISBN:9798400707858
DOI:10.1145/3624062
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: 12 November 2023

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