The maximum entropy method (MEM) is a well known deconvolution technique in radio-interferometry. This method solves a non-linear optimization problem with an entropy regularization term. Other heuristics such as CLEAN are faster but highly user dependent. Nevertheless, MEM has the following advantages: it is unsupervised, it has an statistical basis, it has a better resolution and better image quality under certain conditions. This work presents a high performance GPU version of non-gridded MEM, which is tested using interferometric and simulated data. We propose a single-GPU and a multi-GPU implementation for single and multi-spectral data, respectively. We also make use of the Peer-to-Peer and Unified Virtual Addressing features of newer GPUs which allows to exploit transparently and efficiently multiple GPUs. Several ALMA data sets are used to demonstrate the effectiveness in imaging and to evaluate GPU performance. The results show that a speedup from 1000 to 5000 times faster than a sequential version can be achieved, depending on data and image size. This has allowed us to reconstruct the HD142527 CO(6-5) short baseline data set in 2.1 minutes, instead of the 2.5 days that takes on CPU.
The next images show the image synthesis results using MEM and CLEAN. First column shows MEM model images. Second column shows MEM Restored images, and third column shows CLEAN images. (a)-(c) SDP 8.1 on Band 7, (d)-(f) HL Tau on Band 6. (g)-(i) Antennae Galaxies Northern mosaic on Band 7.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Finally the following image shows the average time per iteration using multi-GPU, and SDP 8.1 Band 7 and HL Tau Band 6 data sets
Author: Miguel Carcamo