Campus Units
Electrical and Computer Engineering, Mathematics
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2019
Journal or Book Title
arXiv
Abstract
Many big data algorithms executed on MapReduce-like systems have a shuffle phase that often dominates the overall job execution time. Recent work has demonstrated schemes where the communication load in the shuffle phase can be traded off for the computation load in the map phase. In this work, we focus on a class of distributed algorithms, broadly used in deep learning, where intermediate computations of the same task can be combined. Even though prior techniques reduce the communication load significantly, they require a number of jobs that grows exponentially in the system parameters. This limitation is crucial and may diminish the load gains as the algorithm scales. We propose a new scheme which achieves the same load as the state-of-the-art while ensuring that the number of jobs as well as the number of subfiles that the data set needs to be split into remain small.
Copyright Owner
The Authors
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Konstantinidis, Konstantinos and Ramamoorthy, Aditya, "CAMR: Coded Aggregated MapReduce" (2019). Electrical and Computer Engineering Publications. 204.
https://lib.dr.iastate.edu/ece_pubs/204
Included in
Computer Sciences Commons, Signal Processing Commons, Systems and Communications Commons
Comments
This is a pre-print of the article Konstantinidis, Konstantinos and Aditya Ramamoorthy. "CAMR: Coded Aggregated MapReduce." arXiv preprint arXiv:1901.07418 (2019). Posted with permission.