Parallel graph algorithms in constant adaptive rounds: theory meets practice
Name
3424573.3424579.pdf
Size
773.71 KB
Format
Adobe PDF
Checksum (MD5)
f349b212419ae5aba403d646fec8406f
Author(s)
Behnezhad, Soheil
Dhulipala, Laxman
Esfandiari, Hossein
Lacki, Jakub
Mirrokni, Vahab
Schudy, Warren
Date Issued
September 2020
Publisher
VLDB Endowment
Citation
Behnezhad, Soheil, Dhulipala, Laxman, Esfandiari, Hossein, Lacki, Jakub, Mirrokni, Vahab et al. 2020. "Parallel graph algorithms in constant adaptive rounds: theory meets practice." 13 (13).
Version
Final published version
Abstract
We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we focus on the Adaptive Massively Parallel Computation (AMPC) model, which is a theoretical model that captures MapReduce-like computation augmented with a distributed hash table.
We show the first AMPC algorithms for all of the studied problems that run in a constant number of rounds and use only O(nϵ) space per machine, where 0 < ϵ < 1. Our results improve both upon the previous results in the AMPC model, as well as the best-known results in the MPC model, which is the theoretical model underpinning many popular distributed computation frameworks, such as MapReduce, Hadoop, Beam, Pregel and Giraph.
Finally, we provide an empirical comparison of the algorithms in the MPC and AMPC models in a fault-tolerant distributed computation environment. We empirically evaluate our algorithms on a set of large real-world graphs and show that our AMPC algorithms can achieve improvements in both running time and round-complexity over optimized MPC baselines.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/3424573.3424579
