论文标题

有效的宽场无线电干涉响应

Efficient wide-field radio interferometry response

论文作者

Arras, Philipp, Reinecke, Martin, Westermann, Rüdiger, Enßlin, Torsten A.

论文摘要

无线电干涉仪不会直接测量天空亮度分布,而是对其进行修改的傅立叶变换。因此,无论特定选择的成像算法如何,成像算法都需要线性测量算子及其伴随的计算表示。在本文中,我们介绍了基于“改进的$ W $堆积”的宽场测量值的无线电干涉测量算子的C ++实现。 It can provide high accuracy (down to $\approx 10^{-12}$), is based on a new gridding kernel which allows smaller kernel support for given accuracy, dynamically chooses kernel, kernel support and oversampling factor for maximum performance, uses piece-wise polynomial approximation for cheap evaluations of the gridding kernel, treats the visibilities in cache-friendly order, uses explicit vectorisation if可用并带有平行方案,该方案也可以很好地沿伴随方向扩展(这对于许多以前的实现来说是一个问题)。该实现的内存足迹很小,因为临时内部数据结构比各自的输入和输出数据小得多,从而可以在内存处理数据集中的内存处理,这些数据集需要从磁盘上读取或以前在几个计算节点上分发。

Radio interferometers do not measure the sky brightness distribution directly but rather a modified Fourier transform of it. Imaging algorithms, thus, need a computational representation of the linear measurement operator and its adjoint, irrespective of the specific chosen imaging algorithm. In this paper, we present a C++ implementation of the radio interferometric measurement operator for wide-field measurements which is based on "improved $w$-stacking". It can provide high accuracy (down to $\approx 10^{-12}$), is based on a new gridding kernel which allows smaller kernel support for given accuracy, dynamically chooses kernel, kernel support and oversampling factor for maximum performance, uses piece-wise polynomial approximation for cheap evaluations of the gridding kernel, treats the visibilities in cache-friendly order, uses explicit vectorisation if available and comes with a parallelisation scheme which scales well also in the adjoint direction (which is a problem for many previous implementations). The implementation has a small memory footprint in the sense that temporary internal data structures are much smaller than the respective input and output data, allowing in-memory processing of data sets which needed to be read from disk or distributed across several compute nodes before.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源