  The Cerebras Wafer Scale Engine (WSE) is an accelerator that combines
hundreds of thousands of AI-cores onto a single chip. Whilst this technology
has been designed for machine learning workloads, the significant amount of
available raw compute means that it is also a very interesting potential target
for accelerating traditional HPC computational codes. Many of these algorithms
are stencil-based, where update operations involve contributions from
neighbouring elements, and in this paper we explore the suitability of this
technology for such codes from the perspective of an early adopter of the
technology, compared to CPUs and GPUs. Using TensorFlow as the interface, we
explore the performance and demonstrate that, whilst there is still work to be
done around exposing the programming interface to users, performance of the WSE
is impressive as it out performs four V100 GPUs by two and a half times and two
Intel Xeon Platinum CPUs by around 114 times in our experiments. There is
significant potential therefore for this technology to play an important role
in accelerating HPC codes on future exascale supercomputers.
