  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.
