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Maciej Besta, Simon Weber, Lukas Gianinazzi, Robert Gerstenberger, Andrey Ivanov, Yishai Oltchik, Torsten Hoefler:
  Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics
(In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), Nov. 2019) Best Paper Finalist, Best Student Paper Finalist
AbstractWe propose Slim Graph: the first programming model and
framework for practical lossy graph compression that facilitates
highperformance 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.
Documentsdownload article: download slides:   BibTeX  @inproceedings{, author={Maciej Besta and Simon Weber and Lukas Gianinazzi and Robert Gerstenberger and Andrey Ivanov and Yishai Oltchik and Torsten Hoefler}, title={{Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics}}, year={2019}, month={Nov.}, booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19)}, source={http://www.unixer.de/~htor/publications/}, } 

