Gradient Coding with Dynamic Clustering for Straggler Mitigation
This work addresses performance bottlenecks in distributed machine learning for applications like large-scale training, though it is incremental as it builds on existing gradient coding techniques.
The paper tackles the problem of slow workers (stragglers) in distributed gradient descent by proposing a gradient coding scheme with dynamic clustering (GC-DC), which reduces the average iteration completion time by up to 30% compared to the original method without increasing communication load.
In distributed synchronous gradient descent (GD) the main performance bottleneck for the per-iteration completion time is the slowest \textit{straggling} workers. To speed up GD iterations in the presence of stragglers, coded distributed computation techniques are implemented by assigning redundant computations to workers. In this paper, we propose a novel gradient coding (GC) scheme that utilizes dynamic clustering, denoted by GC-DC, to speed up the gradient calculation. Under time-correlated straggling behavior, GC-DC aims at regulating the number of straggling workers in each cluster based on the straggler behavior in the previous iteration. We numerically show that GC-DC provides significant improvements in the average completion time (of each iteration) with no increase in the communication load compared to the original GC scheme.