research-article

Slim graph: practical lossy graph compression for approximate graph processing, storage, and analytics

Authors:

Lukas Gianinazzi,

Robert Gerstenberger,

Yishai Oltchik,

Torsten HoeflerAuthors Info & Claims

SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 35, Pages 1 - 25

https://doi.org/10.1145/3295500.3356182

Published: 17 November 2019 Publication History

Abstract

We propose Slim Graph: the first programming model and framework for practical lossy graph compression that facilitates high-performance approximate graph processing, storage, and analytics. Slim Graph enables the developer to express numerous compression schemes using small and programmable compression kernels that can access and modify local parts of input graphs. Such kernels are executed in parallel by the underlying engine, isolating developers from complexities of parallel programming. Our kernels implement novel graph compression schemes that preserve numerous graph properties, for example connected components, minimum spanning trees, or graph spectra. Finally, Slim Graph uses statistical divergences and other metrics to analyze the accuracy of lossy graph compression. We illustrate both theoretically and empirically that Slim Graph accelerates numerous graph algorithms, reduces storage used by graph datasets, and ensures high accuracy of results. Slim Graph may become the common ground for developing, executing, and analyzing emerging lossy graph compression schemes.

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SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2019

1921 pages

ISBN:9781450362290

DOI:10.1145/3295500

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Michela Taufer,
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