高分辨率射电天文成像的近记忆加速
Near Memory Acceleration on High Resolution Radio Astronomy Imaging
摘要：
像平方公里阵列（SKA）这样的现代射电望远镜将需要实时处理艾字节的射电天文信号，以构建高分辨率的天空地图。近存储计算（NMC）可以缓解现有射电天文成像算法中由于频繁访问存储器而导致的性能瓶颈。在本文中，我们表明，一个子模块执行二维快速傅立叶变换（2D FFT）是内存限制使用CPI击穿分析IBM Power9。然后，我们在FPGA上提出了一种用于2D FFT的NMC方法，该方法的性能优于CPU高达120倍，并在使用更少的带宽和内存的同时执行与高端GPU的并行处理。
英文摘要：
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.
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