• Home
• People
• Jobs
• Visit
• Thesis Topics
• Publications
• Conference Rankings
• Research
• Bcast
• Teaching
• Contact

SPONSORS

COLLABORATIONS

The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Publications of SPCL

M. Besta, D. Stanojevic, T. Zivic, J. Singh, M. Hoerold, T. Hoefler:
	Log(Graph): A Near-Optimal High-Performance Graph Representation (presented in Limassol, Cyprus, ACM, Nov. 2018, Accepted at the 27th International Conference on Parallel Architectures and Compilation (PACT'18) ) Abstract Today’s graphs used in domains such as machine learning or social network analysis may contain hundreds of billions of edges. Yet, they are not necessarily stored efficiently, and standard graph representations such as adjacency lists waste a significant number of bits while graph compression schemes such as WebGraph often require time-consuming decompression. To address this, we propose Log(Graph): a graph representation that combines high compression ratios with very low-overhead decompression to enable cheaper and faster graph processing. The key idea is to encode a graph so that the parts of the representation approach or match the respective storage lower bounds. We call our approach “graph logarithmization” because these bounds are usually logarithmic. Our high-performance Log(Graph) implementation based on modern bitwise operations and state-of-the-art succinct data structures achieves high compression ratios as well as performance. For example, compared to the tuned Graph Algorithm Processing Benchmark Suite (GAPBS), it reduces graph sizes by 20-35% while matching GAPBS’ performance or even delivering speedups due to reducing amounts of transferred data. It approaches the compression ratio of the established WebGraph compression library while enabling speedups of up to more than 2×. Log(Graph) can improve the design of various graph processing engines or libraries on single NUMA nodes as well as distributed-memory systems. Documents download article: access preprint on arxiv: download slides: Recorded talk (best effort)
	BibTeX
@inproceedings{,   author={Maciej Besta and Dimitri Stanojevic and T. Zivic and J. Singh and M. Hoerold and Torsten Hoefler},   title={{Log(Graph): A Near-Optimal High-Performance Graph Representation}},   year={2018},   month={11},   location={Limassol, Cyprus},   publisher={ACM},   note={Accepted at the 27th International Conference on Parallel Architectures and Compilation (PACT'18)}, }