Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable
Name
spaa2018.pdf
Description
Accepted version
Size
825.22 KB
Format
Adobe PDF
Checksum (MD5)
e6b3e1f359e800a105e8d158acd59221
Author(s)
Dhulipala, Laxman
Blelloch, Guy E
Shun, Julian
Date Issued
2018
Journal
Annual ACM Symposium on Parallelism in Algorithms and Architectures
Publisher
Association for Computing Machinery (ACM)
Version
Author's final manuscript
Abstract
© 2018 Association for Computing Machinery. There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 13 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We provide a publicly-available benchmark suite containing our implementations.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3210377.3210414
