Design flow for GPU and multicore execution of dynamic dataflow programs. Boutellier, J. & Nyländen, T. Journal of Signal Processing Systems, 89(3):469-478, 2017. abstract bibtex Data flow programming has received increasing attention in the age of multicore and heterogeneous computing. Modular and concurrent data flow program descriptions enable highly automated approaches for design space exploration, optimization and deployment
of applications. A great advance in data
flow programming has been the recent introduction of the RVC-CAL language. Having been standardized by the ISO, the RVC-CAL data flow language provides a solid basis for the development of tools, design methodologies and design
flows. This paper proposes a novel design flow
for mapping RVC-CAL data flow programs to parallel and heterogeneous execution platforms. Through the proposed design flow the programmer can describe an application in the RVC-CAL language and map it to multi- and many-core platforms, as well as GPUs, for efficient execution. The functionality and efficiency of
the proposed approach is demonstrated by a parallel implementation of a video processing application and a run-time reconfi
gurable
filter for telecommunications. Experiments are performed on GPU and multicore platforms with up to 16 cores, and the results show that for high-performance applications the proposed design
fow provides up to 4x higher throughput than the state-of-the-art approach in multicore execution of RVC-CAL programs.
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abstract = {Data flow programming has received increasing attention in the age of multicore and heterogeneous computing. Modular and concurrent data flow program descriptions enable highly automated approaches for design space exploration, optimization and deployment
of applications. A great advance in data
flow programming has been the recent introduction of the RVC-CAL language. Having been standardized by the ISO, the RVC-CAL data flow language provides a solid basis for the development of tools, design methodologies and design
flows. This paper proposes a novel design flow
for mapping RVC-CAL data flow programs to parallel and heterogeneous execution platforms. Through the proposed design flow the programmer can describe an application in the RVC-CAL language and map it to multi- and many-core platforms, as well as GPUs, for efficient execution. The functionality and efficiency of
the proposed approach is demonstrated by a parallel implementation of a video processing application and a run-time reconfi
gurable
filter for telecommunications. Experiments are performed on GPU and multicore platforms with up to 16 cores, and the results show that for high-performance applications the proposed design
fow provides up to 4x higher throughput than the state-of-the-art approach in multicore execution of RVC-CAL programs.},
bibtype = {article},
author = {Boutellier, J and Nyländen, T},
journal = {Journal of Signal Processing Systems},
number = {3}
}
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