Wideband Channelization for Software-Defined Radio via Mobile Graphics Processors

Vignesh Adhinarayanan, Wu-chun Feng
Department of Computer Science and Department of Electrical & Computer Engineering, NSF Center for High-Performance Reconfigurable Computing, Virginia Tech, Blacksburg, Virginia, U.S.A.
19th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2013), 2013


   author={Adhinarayanan, Vignesh and Feng, Wu-chun},

   title={Wideband Channelization for Software-Defined Radio via Mobile Graphics Processors},

   booktitle={19th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2013)},

   address={Seoul, Korea},




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Wideband channelization is a computationally intensive task within software-defined radio (SDR). To support this task, the underlying hardware should provide high performance and allow flexible implementations. Traditional solutions use field-programmable gate arrays (FPGAs) to satisfy these requirements. While FPGAs allow for flexible implementations, realizing a FPGA implementation is a difficult and time-consuming process. On the other hand, multicore processors while more programmable, fail to satisfy performance requirements. Graphics processing units (GPUs) overcome the above limitations. However, traditional GPUs are power-hungry and can consume as much as 350 watts, making them ill-suited for many SDR environments, particularly those that are battery-powered. Here we explore the viability of low-power mobile graphics processors to simultaneously overcome the limitations of performance, flexibility, and power. Via execution profiling and performance analysis, we identify major bottlenecks in mapping the wideband channelization algorithm onto these devices and adopt several optimization techniques to achieve multiplicative speed-up over a multithreaded implementation. Overall, our approach delivers a speedup of up to 43-fold on the discrete AMD Radeon HD 6470M GPU and 27-fold on the integrated AMD Radeon HD 6480G GPU, when compared to a vectorized and multithreaded version running on the AMD A4-3300M CPU.
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