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Current and upcoming radio-interferometers are expected to produce volumes ofdata of increasing size that need to be processed in order to generate thecorresponding sky brightness distributions through imaging. This represents anoutstanding computational challenge, especially when large fields of viewand/or high resolution observations are processed. We have investigated theadoption of modern High Performance Computing systems specifically addressingthe gridding, FFT-transform and w-correction of imaging, combining parallel andaccelerated solutions. We have demonstrated that the code we have developed cansupport dataset and images of any size compatible with the available hardware,efficiently scaling up to thousands of cores or hundreds of GPUs, keeping thetime to solution below one hour even when images of the size of the order ofbillion or tens of billion of pixels are generated. In addition, portabilityhas been targeted as a primary objective, both in terms of usability ondifferent computing platforms and in terms of performance. The presentedresults have been obtained on two different state-of-the-art High PerformanceComputing architectures.

High Performance W-stacking for Imaging Radio Astronomy Data: a Parallel and Accelerated Solution