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Data shuffling between distributed workers is one of the critical steps in implementing large-scale learning algorithms. The focus of this work is to understand the fundamental trade-off between the amount of storage and the communication overhead for distributed data shuffling. We first present an information theoretic formulation for the data shuffling problem, accounting for the underlying problem parameters (i.e., number of workers, K, number of data points, N, and the available storage, S per node). Then, we derive an information theoretic lower bound on the communication overhead for data shuffling as a function of these parameters. Next, we present a novel coded communication scheme and show that the resulting communication overhead of the proposed scheme is within a multiplicative factor of at most 2 from the lower bound. Furthermore, we introduce an improved aligned coded shuffling scheme, which achieves the optimal storage vs communication trade-off for K <; 5, and further reduces the maximum multiplicative gap down to 7/6, for K ≥ 5.
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Information Theoretic Limits of Data Shuffling for Distributed Learning
Data shuffling is one of the fundamental building blocks for distributed learning algorithms, that increases the statistical gain for each step of the learning process. In each iteration, different shuffled data points are assigned by a central node to a distributed set of workers to perform local computations, which leads to communication bottlenecks. The focus of this paper is on formalizing and understanding the fundamental information-theoretic tradeoff between storage (per worker) and the worst-case communication overhead for the data shuffling problem. We completely characterize the information theoretic tradeoff for K = 2, and K = 3 workers, for any value of storage capacity, and show that increasing the storage across workers can reduce the communication overhead by leveraging coding. We propose a novel and systematic data delivery and storage update strategy for each data shuffle iteration, which preserves the structural properties of the storage across the workers, and aids in minimizing the communication overhead in subsequent data shuffling iterations.
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- Award ID(s):
- 1651492
- PAR ID:
- 10048924
- Date Published:
- Journal Name:
- IEEE Globecom
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GLOCOM.2016.7841903
