Methodology for parallelising linked data?
I'd look into applying some kind of sliding window to you data so you can process one subset at the time. By adjusting the window overlapping with previous chunks you can control how much context have on each batch.

The implementation details will depend on what kind of data structure you use, but since you mentioned a grid in which you want to smear the data, a convolution+pooling approach might do the trick. It's very easy to run in parallel and can be super efficiently on modern GPUs.

Also, you example suggests that the result of your chunk processing would be some kind of subsampling. If the data is too large you could subsample it first. There are many ways to do it depending on which criteria is acceptable for discarding data points so you'll have to dig into it to figure out which method is best for your use case