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- W2022502450 abstract "Hybrid core computing, with CPUs augmented with FPGAs and/or GPUs, offers a promising pathway of addressing emerging high-performance 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 the availability of dedicated FPGA resources: DSP48E slices, block RAMs and FPGA I/O banks in particular. Provided 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 data-width architecture. Results show a trade-off with respect to performance, memory input/output and available device resources when choosing the right accelerators for a particular application." @default.
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- W2022502450 date "2011-11-01" @default.
- W2022502450 modified "2023-09-24" @default.
- W2022502450 title "Spectral Method Characterization on FPGA and GPU Accelerators" @default.
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- W2022502450 doi "https://doi.org/10.1109/reconfig.2011.83" @default.
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