  The recent Natural Language Processing techniques have been refreshing the
state-of-the-art performance at an incredible speed. Training huge language
models is therefore an imperative demand in both industry and academy. However,
huge language models impose challenges to both hardware and software. Graphical
processing units (GPUs) are iterated frequently to meet the exploding demand,
and a variety of ASICs like TPUs are spawned. However, there is still a tension
between the fast growth of the extremely huge models and the fact that Moore's
law is approaching the end. To this end, many model parallelism techniques are
proposed to distribute the model parameters to multiple devices, so as to
alleviate the tension on both memory and computation. Our work is the first to
introduce a 3-dimensional model parallelism for expediting huge language
models. By reaching a perfect load balance, our approach presents smaller
memory and communication cost than existing state-of-the-art 1-D and 2-D model
parallelism. Our experiments on 64 TACC's V100 GPUs show that our 3-D
parallelism outperforms the 1-D and 2-D parallelism with 2.32x and 1.57x
speedup, respectively.
