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

TensorFlow as a DSL for stencil-based computation on the Cerebras Wafer Scale Engine