论文标题
高分辨率射电天文学成像上的近乎记忆加速
Near Memory Acceleration on High Resolution Radio Astronomy Imaging
论文作者
论文摘要
平方公里阵列（SKA）之类的现代射电望远镜将需要实时处理无线电 - 媒介物信号，以构建天空的高分辨率地图。由于最先进的放射媒体成像算法中的频繁记忆访问，近序列计算（NMC）可以减轻性能瓶颈。在本文中，我们表明，执行二维快速傅立叶变换（2D FFT）的子模块使用IBM Power上的CPI分解分析结合了内存9。然后，我们在2D FFT上提出了FPGA上的NMC方法，该方法的表现胜过120倍的CPU，并且使用较少的带宽和内存，并与高端GPU相当地执行。
Modern radio telescopes like the Square Kilometer Array (SKA) will need to process in real-time exabytes of radio-astronomical signals to construct a high-resolution map of the sky. Near-Memory Computing (NMC) could alleviate the performance bottlenecks due to frequent memory accesses in a state-of-the-art radio-astronomy imaging algorithm. In this paper, we show that a sub-module performing a two-dimensional fast Fourier transform (2D FFT) is memory bound using CPI breakdown analysis on IBM Power9. Then, we present an NMC approach on FPGA for 2D FFT that outperforms a CPU by up to a factor of 120x and performs comparably to a high-end GPU, while using less bandwidth and memory.
