  While performing distributed computations in today's cloud-based platforms,
execution speed variations among compute nodes can significantly reduce the
performance and create bottlenecks like stragglers. Coded computation
techniques leverage coding theory to inject computational redundancy and
mitigate stragglers in distributed computations. In this paper, we propose a
dynamic workload distribution strategy for coded computation called Slack
Squeeze Coded Computation ($S^2C^2$). $S^2C^2$ squeezes the compute slack
(i.e., overhead) that is built into the coded computing frameworks by
efficiently assigning work for all fast and slow nodes according to their
speeds and without needing to re-distribute data. We implement an LSTM-based
speed prediction algorithm to predict speeds of compute nodes. We evaluate
$S^2C^2$ on linear algebraic algorithms, gradient descent, graph ranking, and
graph filtering algorithms. We demonstrate 19% to 39% reduction in total
computation latency using $S^2C^2$ compared to job replication and coded
computation. We further show how $S^2C^2$ can be applied beyond matrix-vector
multiplication.
