  With their widespread availability, FPGA-based accelerators cards have become
an alternative to GPUs and CPUs to accelerate computing in applications with
certain requirements (like energy efficiency) or properties (like fixed-point
computations). In this paper we show results and experiences from mapping an
industrial application used for drug discovery on several types of
accelerators. We especially highlight the effort versus benefit of FPGAs
compared to CPUs and GPUs in terms of performance and energy efficiency. For
this application, even with extensive use of FPGA-specific features, and
performing different optimizations, results on GPUs are still better, both in
terms of energy and performance.
