Spectral Method Characterization on FPGA and GPU Accelerators
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}
}
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.
January 15, 2012 by hgpu
