6934

Spectral Method Characterization on FPGA and GPU Accelerators

Karl Pereira, Peter Athanas, Heshan Lin, Wu Feng
Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg,USA
International Conference on ReConFigurable Computing and FPGAs (ReConFig’11), 2011

@inproceedings{pereira2011spectral,

   title={Spectral Method Characterization on FPGA and GPU Accelerators},

   author={Pereira, K. and Athanas, P. and Lin, H. and Feng, W.},

   booktitle={2011 International Conference on Reconfigurable Computing and FPGAs},

   pages={487–492},

   year={2011},

   organization={IEEE}

}

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As CPU clock frequencies plateau and the doubling of CPU cores per processor exacerbate the memory wall, hybrid core computing, utilizing CPUs augmented with FPGAs and/or GPUs holds the promise of addressing highperformance computing demands, particularly with respect to performance, power and productivity. This paper compares the sustained performance of a complex, single precision, floating-point, 1D, Fast Fourier Transform (FFT) implementation on state-of-the-art FPGA and GPU accelerators. As results show, FPGA floating-point performance is highly sensitive to a mix of dedicated FPGA resources; DSP48E slices, block RAMs and FPGA I/O banks in particular. Estimated results show that for the floating-point FFT benchmark on FPGAs, these resources are the performance limiting factor. For fixed-point FFTs, however, FPGAs exploit a flexible data path width to trade-off circuit cost with speed of computation in applications requiring smaller precision to improve performance, power and device utilization. GPUs cannot fully take advantage of this, having a fixed datawidth architecture.
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