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Massively parallel architectures offer the potential to significantlyaccelerate an application relative to their serial counterparts. However, notall applications exhibit an adequate level of data and/or task parallelism toexploit such platforms. Furthermore, the power consumption associated withthese forms of computation renders "scaling out" for exascale levels ofperformance incompatible with modern sustainable energy policies. In this work,we investigate the potential for field-programmable gate arrays (FPGAs) tofeature in future exascale platforms, and their capacity to improve performanceper unit power measurements for the purposes of scientific computing. We havefocussed our efforts on Variational Monte Carlo, and report on the benefits ofco-processing with an FPGA relative to a purely multicore system.

Field-Programmable Gate Arrays and Quantum Monte Carlo: Power Efficient Co-processing for Scalable High-Performance Computing