Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics
Maciej Besta, Simon Weber, Lukas Gianinazzi, Robert Gerstenberger,, Andrey Ivanov, Yishai Oltchik, Torsten Hoefler

TL;DR
Slim Graph introduces a flexible framework for practical lossy graph compression that accelerates graph processing, reduces storage, and maintains high accuracy through programmable kernels and statistical analysis.
Contribution
It presents the first programmable framework for lossy graph compression that supports high-performance approximate processing and property preservation.
Findings
Accelerates graph algorithms significantly.
Reduces storage requirements for large graph datasets.
Maintains high accuracy in approximate graph analytics.
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…
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Taxonomy
TopicsGraph Theory and Algorithms · Interconnection Networks and Systems · Algorithms and Data Compression
