  Massively parallel architectures offer the potential to significantly
accelerate an application relative to their serial counterparts. However, not
all applications exhibit an adequate level of data and/or task parallelism to
exploit such platforms. Furthermore, the power consumption associated with
these forms of computation renders "scaling out" for exascale levels of
performance incompatible with modern sustainable energy policies. In this work,
we investigate the potential for field-programmable gate arrays (FPGAs) to
feature in future exascale platforms, and their capacity to improve performance
per unit power measurements for the purposes of scientific computing. We have
focussed our efforts on Variational Monte Carlo, and report on the benefits of
co-processing with an FPGA relative to a purely multicore system.
