  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.
