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ANSWER: Yes. Using ppn=4 and -n 4 and tracking the cores given in the cgroup, I was able to see the same sort of variance in images. For example a job with 4 even cores produced a different image than a job with 3 even and 1 odd core. So, it doesn't look like Torque does anything significantly different than my manual tests and there is no guarentee as to which cores Torque will give you.
QUESTION: We need to track down where divergences are occurring in the imaging. To date we've been looking at the final image, now we need to see is it in the dirty image, the weights, the cleaned image etc to try and isolate the cause.
- Select data set (jr-template)
- Select a parallelization scheme, I'd suggest 8 cores with -n 8 and then use a 8-0 and 6-2 mix to get a good and bad image
- Set up imaging so that it preserves the residual pre-normalization and post normalization, the weights and sumwt and the cleaned image.
- Limit imaging to one imaging cycle