We focus on sorting, which is the building block of many machine learning algorithms, and propose a novel distributed sorting algorithm, named Coded TeraSort, which substantially improves the execution time of the TeraSort benchmark in Hadoop MapReduce. The key idea of Coded TeraSort is to impose structured redundancy in data, in order to enable in-network coding opportunities that overcome the data shuffling bottleneck of TeraSort. We empirically evaluate the performance of CodedTeraSort algorithm on Amazon EC2 clusters, and demonstrate that it achieves 1.97x - 3.39x speedup, compared with TeraSort, for typical settings of interest.
@article{arxiv.1702.04850,
title = {Coded TeraSort},
author = {Songze Li and Sucha Supittayapornpong and Mohammad Ali Maddah-Ali and A. Salman Avestimehr},
journal= {arXiv preprint arXiv:1702.04850},
year = {2017}
}
Comments
to appear in proceedings of 2017 International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics