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

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems