High Performance and Distributed Computing

The SGI Power Challenge Array of NCSA

Astronomical Data Reduction on Parallel Computers

The aperture synthesis technique makes it possible to achieve high resolution without having to build radio telescopes tens of kilometers in diameter. An array of a large number of antennas all interconnected as interferometers measures simultaneously a large number of spatial frequency Fourier components; the radio image (with an angular resolution determined by the separation of the individual antennas) is then formed in a digital computer by Fourier transformation of the observed visibilities. Because the images produced by a synthesis array are formed in a digital computer by Fourier transformation of the observed visibilities, the computer system is an integral part of the synthesis array telescope - its image-forming element. The computational requirements are not driven solely by the single FFT of the observed visibilities required to produce an image, but by algorithms developed to overcome two factors which greatly degrade performance. Sparse sampling of the aperture plane results in strong sidelobes (i.e., large spurious responses away from the principal maximum); this instrumental signature may be removed by computationally intensive non-linear deconvolution techniques. Time varying systematic errors due to instrumental and atmospheric instabilities also degrade image quality; a powerful but computationally intensive method called self-calibration of correcting for rapid atmospheric and instrumental distortions has been developed. Together, deconvolution and self-calibration techniques can provide several orders of magnitude improvement in the fidelity of images produced by radio synthesis arrays.

Many of the most computationally intensive observations are spectral line observations, where hundreds or thousands of images of the same region are to be obtained simultaneously, each at a slightly shifted frequency. Because each of these images may be treated as independent from the others, this is a completely parallelizable problem. The solution is simply to have each of the available processors handle the image construction for one of the spectral line channels. If the number of processors is less than the number of channels, a scheduling system can keep processors supplied with new data until all spectral channels have been processed. The separate results can then be combined into a single data cube for visualization and analysis. We have focussed on using the NCSA SGI Power Challenge for this problem.

IMAGER

Initial work has been with the radio synthesis array software system SDE (Software Development Environment). The SDE code is implimented in a parallel fashion with the newly-developed IMAGER package. An HTML and a PostScript version of the IMAGER documentation is available. Code for gridding observed visibility data, Fourier transforming the data into the image plane, and performing a non-linear deconvolution on the 16 processor SGI R8000 system at NCSA has been compared with results for a single processor R4400 system. The amount of input data is about 5 gigabytes. The table gives a timing comparison. We are currently extending this system to run on the 64-processor SGI Power Challenge Array.

Number of Processors Used	Type of Processor	Total Wall Clock Time
1	R4400	1200 minutes
16	R8000	50 minutes

IMAGER on the SGI Cray Origin2000

We have carried out timing tests of IMAGER on the SGI Cray Origin2000. For a class of spectral line problems the speed-up is nearly linear with the number of processors. Detailed timing results are available.