Current and upcoming radio-interferometers are expected to produce volumes of data of increasing size that need to be processed in order to generate the corresponding sky brightness distributions through imaging. This represents an outstanding computational challenge, especially when large fields of view and/or high resolution observations are processed. We have investigated the adoption of modern High Performance Computing systems specifically addressing the gridding, FFT-transform and w-correction of imaging, combining parallel and accelerated solutions. We have demonstrated that the code we have developed can support dataset and images of any size compatible with the available hardware, efficiently scaling up to thousands of cores or hundreds of GPUs, keeping the time to solution below one hour even when images of the size of the order of billion or tens of billion of pixels are generated. In addition, portability has been targeted as a primary objective, both in terms of usability on different computing platforms and in terms of performance. The presented results have been obtained on two different state-of-the-art High Performance Computing architectures.

翻译：目前和即将到来的无线电干涉计将产生数量越来越大的数据,需要加以处理,以便通过成像产生相应的天空亮度分布。这是一个突出的计算挑战,特别是在处理大视野和(或)高分辨率观测时。我们调查了采用现代高性能计算系统,具体处理成像的网格、FFFT转换和W校正,同时采用加速的解决办法。我们已经证明,我们开发的代码可以支持与现有硬件兼容的任何尺寸的数据集和图像,有效地将核心扩大至数千个或数百个GPU,即使产生了10亿或数十亿像素大小的图像,也把时间维持在1小时以下。此外,从不同计算平台的可用性和性能两方面来看,移动性被作为主要目标,在两种不同的高级性能电子计算结构中取得了结果。