  The recent trend toward deep learning has led to the development of a variety
of highly innovative AI accelerator architectures. One such architecture, the
Cerebras Wafer-Scale Engine 2 (WSE-2), features 40 GB of on-chip SRAM, making
it a potentially attractive platform for latency- or bandwidth-bound HPC
simulation workloads. In this study, we examine the feasibility of performing
continuous energy Monte Carlo (MC) particle transport on the WSE-2 by porting a
key kernel from the MC transport algorithm to Cerebras's CSL programming model.
New algorithms for minimizing communication costs and for handling load
balancing are developed and tested. The WSE-2 is found to run 130 times faster
than a highly optimized CUDA version of the kernel run on an NVIDIA A100 GPU --
significantly outpacing the expected performance increase given the difference
in transistor counts between the architectures.
