  Heterogeneous computing can potentially offer significant performance and
performance per watt improvements over homogeneous computing, but the question
"what is the ideal mapping of algorithms to architectures?" remains an open
one. In the past couple of years new types of computing devices such as FPGAs
have come into general computing use. In this work we attempt to add to the
body of scientific knowledge by comparing Kernel performance and performance
per watt of seven key algorithms according to Berkley's dwarf taxonomy. We do
so using the Rodinia benchmark suite on three different high-end hardware
architecture representatives from the CPU, GPU and FPGA families. We find
results that support some distinct mappings between the architecture and
performance per watt. Perhaps the most interesting finding is that, for our
specific hardware representatives, FPGAs should be considered as alternatives
to GPUs and CPUs in several key algorithms: N-body simulations, dense linear
algebra and structured grid.
