  In this paper, we evaluate training of deep recurrent neural networks with
half-precision floats. We implement a distributed, data-parallel, synchronous
training algorithm by integrating TensorFlow and CUDA-aware MPI to enable
execution across multiple GPU nodes and making use of high-speed interconnects.
We introduce a learning rate schedule facilitating neural network convergence
at up to $O(100)$ workers.
  Strong scaling tests performed on clusters of NVIDIA Pascal P100 GPUs show
linear runtime and logarithmic communication time scaling for both single and
mixed precision training modes. Performance is evaluated on a scientific
dataset taken from the Joint European Torus (JET) tokamak, containing
multi-modal time series of sensory measurements leading up to deleterious
events called plasma disruptions, and the benchmark Large Movie Review
Dataset~\cite{imdb}. Half-precision significantly reduces memory and network
bandwidth, allowing training of state-of-the-art models with over 70 million
trainable parameters while achieving a comparable test set performance as
single precision.
