Scalable Node Embedding Algorithms Using Distributed Sparse Matrix Operations

We introduce a distributed memory parallel algorithm for force-directed node embedding that places vertices of a graph into a low-dimensional vector space based on the interplay of attraction among neighboring vertices and repulsion among distant vertices. We develop our algorithms using two sparse...

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Bibliographic Details
Published in:2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) pp. 1199 - 1201
Main Authors: Ranawaka, Isuru, Azad, Ariful
Format: Conference Proceeding
Language:English
Published: IEEE 27.05.2024
Subjects:
Conferences
Data transfer
Distributed processing
Embedding
Parallel algorithms
SDDMM
Sparse matrices
Sparse Matrix Computation
SpMM
Supercomputers
Vectors
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Summary:We introduce a distributed memory parallel algorithm for force-directed node embedding that places vertices of a graph into a low-dimensional vector space based on the interplay of attraction among neighboring vertices and repulsion among distant vertices. We develop our algorithms using two sparse matrix operations, SDDMM and SpMM. We propose a configurable pull-push-based communication strategy that optimizes memory usage and data transfers based on computing resources and asynchronous MPI communication to overlap communication and computation. Our algorithm scales up to 256 nodes on distributed supercomputers by surpassing the performance of state-of-the-art algorithms.
DOI:10.1109/IPDPSW63119.2024.00205