Order Optimal Cascaded Code Distributed Computing With Low Complexity and Improved Flexibility
Mingming Zhang, Youlong Wu, Minquan Cheng, and Dianhua Wu

TL;DR
This paper introduces a low-complexity, flexible cascaded coded distributed computing scheme that reduces communication load, requires fewer input files, and operates over binary fields, achieving near-optimal performance.
Contribution
The paper proposes a novel cascaded CDC scheme with reduced data splitting, improved multicast gains, and binary field operations, enhancing efficiency and flexibility.
Findings
Achieves multicast gains close to theoretical maximum.
Requires significantly fewer input files and output functions.
Order optimal within a factor of 2 for large K.
Abstract
Coded distributed computing (CDC), proposed by Li \emph{et al.}, offers significant potential for reducing the communication load in MapReduce computing systems. In cascaded CDC with nodes, input files, and output functions, each input file will be mapped by nodes and each output function will be computed by nodes such that coding techniques can be applied to generate multicast opportunities. However, a significant limitation of most existing coded distributed computing schemes is their requirement to split the original data into a large number of input files (or output functions) that grows exponentially with , which significantly increases the coding complexity and degrades the system performance. In this paper, we focus on the case of , deliberately designing the strategy of data placement and output functions assignment, such that a…
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Privacy-Preserving Technologies in Data · Sparse and Compressive Sensing Techniques
