Efficient Distributed Transposition Of Large-Scale Multigraphs And High-Cardinality Sparse Matrices
Abstract
Graph-based representations underlie a wide range of scientific problems. Graph connectivity is typically represented as a sparse matrix in the Compressed Sparse Row format. Large-scale graphs rely on distributed storage, allocating distinct subsets of rows to compute nodes. Efficient matrix transpose is an operation of high importance, providing the reverse graph pathways and a column-ordered matrix view. This operation is well studied for simple graph models. Nevertheless, its resolution for multigraphs and higher-cardinality connectivity matrices is unexistent. We advance state-of-the-art distributed transposition methods by providing a theoretical model, algorithmic details, MPI-based implementation and proof of mathematical soundness for such complex models. Benchmark results demonstrate ideal and almost ideal scaling properties for perfectly- and heterogeneously-balanced datasets, respectively
Cite
@article{arxiv.2012.06012,
title = {Efficient Distributed Transposition Of Large-Scale Multigraphs And High-Cardinality Sparse Matrices},
author = {Bruno Magalhaes and Felix Schürmann},
journal= {arXiv preprint arXiv:2012.06012},
year = {2020}
}