Published February 24, 2018
| Version 1
Software
Open
Grazelle-PPoPP18
- 1. Stanford University
- 2. University of California, Santa Cruz
Description
Grazelle is a high-performance hybrid (push-based and pull-based) graph processing framework targeting a single machine containing or more x86-64-based processors. It is the embodiment of the two optimization strategies described in the PPoPP 2018 paper Making Pull-Based Graph Processing Performant.
The content of this repository is intended to support the results presented in the aforementioned paper. It contains all the source code and documentation required to build and run Grazelle as configured for each experiment presented in the paper as well as pre-converted ready-to-use versions of the datasets used in the evaluation.
Files
Grazelle-PPoPP18.zip
Files
(39.0 GB)
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Additional details
Related works
- Is supplement to
- 10.1145/3178487.3178506 (DOI)
References
- Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data. (2014).
- Camil Demetrescu. 2010. 9th DIMACS Implementation Challenge. http://www.dis.uniroma1.it/challenge9/download.shtml. (2010).
- Paolo Boldi, Marco Rosa, Massimo Santini, and Sebastiano Vigna. 2011. Layered Label Propagation: A MultiResolution Coordinate-Free Ordering for Compressing Social Networks. In WWW '11. ACM, 587–596.
- Paolo Boldi and Sebastiano Vigna. 2004. The WebGraph Framework I: Compression Techniques. In WWW '04. ACM, 595–601.