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General Problems

General embarrassingly parallel problems will require the distribution of existing C++ code across multiple processors. Once these routines have been constructed, they can be used in other applications (see § 3.1.2).

• Spectral line image construction and deconvolution. Spectral line processing, including imaging and deconvolution can be carried out easily in an embarrassingly parallel way. This is a common case and is easily implemented. The details of this implementation of an EP case will be important as a template for more complicated problems. This should be the first implementation.
  1. Spectral line cube formation. This is the simplest case, where independent spectral-line channels are sent to separate processors for imaging.

  2. Spectral line deconvolution. Independent spectral-line channels are sent to separate processors for deconvolution. If both imaging and deconvolution are requested by the user, the two functions should be pipelined together and sent to individual processors in one step.

• Linear mosaic algorithm with linear deconvolution (MOSLIN in SDE) together with linear combination of pre-deconvolved images, weighting determined by primary beam. Separate fields are independent and can be sent to different processors.

• Antenna-based determination of calibration and self-calibration. This problem can be separated into independent time slices and sent to individual processors.

• Antenna and baseline-based fringe fitting for a range of spectral channels and fringe rates (normally only for VLBI data). This is a very computationally intensive problem that can be separated in time for parallel processing. $\star$

• Image construction from calibrated total power data (frequency-switched, beam-switched, multi-beam, focal plane array) sequences from single antennas and phased arrays, with and without spectrometers. This can be divided into separate time ranges and sent to separate processors.

• Calibration for non-isoplanicity using special extensions of self-calibration. This is the general case, which includes the wide field imaging, with clusters of fields. Fields could be constructed by shifting the phases of the visibility data then sent to individual processors.

• Parameter-driven automated flagging for large data sets. This could be done by slicing in time. However, flagging operations are usually not computationally intensive, thus the benefit of this is not expected to be great. Low priority.