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State-of-the-art deep learning systems rely on iterative distributed trainingto tackle the increasing complexity of models and input data. The iterationtime in these communication-heavy systems depends on the computation time,communication time and the extent of overlap of computation and communication.  In this work, we identify a shortcoming in systems with graph representationfor computation, such as TensorFlow and PyTorch, that result in high variancein iteration time --- random order of received parameters across workers. Wedevelop a system, TicTac, to improve the iteration time by fixing this issue indistributed deep learning with Parameter Servers while guaranteeingnear-optimal overlap of communication and computation. TicTac identifies andenforces an order of network transfers which improves the iteration time usingprioritization. Our system is implemented over TensorFlow and requires nochanges to the model or developer inputs. TicTac improves the throughput by upto $37.7\%$ in inference and $19.2\%$ in training, while also reducingstraggler effect by up to $2.3\times$. Our code is publicly available.

TicTac: Accelerating Distributed Deep Learning with Communication Scheduling