  The impending termination of Moore's law motivates the search for new forms
of computing to continue the performance scaling we have grown accustomed to.
Among the many emerging Post-Moore computing candidates, perhaps none is as
salient as the Field-Programmable Gate Array (FPGA), which offers the means of
specializing and customizing the hardware to the computation at hand.
  In this work, we design a custom FPGA-based accelerator for a computational
fluid dynamics (CFD) code. Unlike prior work -- which often focuses on
accelerating small kernels -- we target the entire Poisson solver on
unstructured meshes based on the high-fidelity spectral element method (SEM)
used in modern state-of-the-art CFD systems. We model our accelerator using an
analytical performance model based on the I/O cost of the algorithm. We
empirically evaluate our accelerator on a state-of-the-art Intel Stratix 10
FPGA in terms of performance and power consumption and contrast it against
existing solutions on general-purpose processors (CPUs). Finally, we propose a
data movement-reducing technique where we compute geometric factors on the fly,
which yields significant (700+ Gflop/s) single-precision performance and an
upwards of 2x reduction in runtime for the local evaluation of the Laplace
operator.
  We end the paper by discussing the challenges and opportunities of using
reconfigurable architecture in the future, particularly in the light of
emerging (not yet available) technologies.
