傅里叶域去色散
Fourier-domain dedispersion
摘要：
我们提出并实现了傅里叶域解色散（FDD）算法，一个蛮力非相干解色散算法的概念。该算法校正了来自诸如射电望远镜和快速射电暴的源的无线电发射的到达时间中的与频率相关的色散延迟。传统的时域去色散算法使用时移校正时间延迟，FDD算法通过将相位旋转应用于傅立叶变换的时间序列数据来执行这些移位。对许多试验色散测量（DM）的非相干去色散是计算、存储器带宽和I/O密集型的，并且去色散算法已经在图形处理单元（GPU）上实现以实现高计算性能。然而，时域去色散算法具有低运算强度，因此通常是存储器带宽有限的。FDD算法避免了这一限制，并且是计算受限的，提供了一条利用当前和未来几代GPU潜力的途径。我们实现的FDD算法的DEDISP时域去色散软件的扩展。我们比较了使用NVIDIA Titan RTX GPU与标准版以及优化版DEDISP实现FDD的性能和完成能耗。与原始算法相比，优化的实现已经提供了1.5到2倍的加速比，而能量利用率仅为66%。我们发现，FDD算法优于优化的时域去色散算法的性能和5%的能量完成时，需要大量的DM（>=512）的另一个20%。FDD算法为无线电基站的基于FFT的周期性调查提供了额外的性能改进，因为可以省略回到时域的FFT。我们预计，这种计算性能的增益将在未来进一步提高。
英文摘要：
We present and implement the concept of the Fourier-domain dedispersion (FDD) algorithm, a brute-force incoherent dedispersion algorithm. This algorithm corrects the frequency-dependent dispersion delays in the arrival time of radio emission from sources such as radio pulsars and fast radio bursts. Where traditional time-domain dedispersion algorithms correct time delays using time shifts, the FDD algorithm performs these shifts by applying phase rotations to the Fourier-transformed time-series data. Incoherent dedispersion to many trial dispersion measures (DMs) is compute, memory-bandwidth and I/O intensive and dedispersion algorithms have been implemented on Graphics Processing Units (GPUs) to achieve high computational performance. However, time-domain dedispersion algorithms have low arithmetic intensity and are therefore often memory-bandwidth limited. The FDD algorithm avoids this limitation and is compute limited, providing a path to exploit the potential of current and upcoming generations of GPUs. We implement the FDD algorithm as an extension of the DEDISP time-domain dedispersion software. We compare the performance and energy-to-completion of the FDD implementation using an NVIDIA Titan RTX GPU against the standard as well as an optimized version of DEDISP. The optimized implementation already provides a factor of 1.5 to 2 speedup at only 66% of the energy utilization compared to the original algorithm. We find that the FDD algorithm outperforms the optimized time-domain dedispersion algorithm by another 20% in performance and 5% in energy-to-completion when a large number of DMs (>=512) are required. The FDD algorithm provides additional performance improvements for FFT-based periodicity surveys of radio pulsars, as the FFT back to the time domain can be omitted. We expect that this computational performance gain will further improve in the future.
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