Pipeline, Geometric Decomposition, Data Parallelism

What's interesting about the pipeline pattern is that, when properly constructed it works as a wonderful platform to use of further patterns. The paper mentions that we can use things like the discrete events pattern from earlier this week to pass messages between the stages of the pipeline, which of course also allows us to use the Fork-Join framework to ease the overhead as pointed in the earlier paper. This idea could also be used, depending on the resource requirements, to have multiple full pipelines running in multiple processors, though without investigation I would assume that it is actually more efficient to have multiple instances of the individual stages working in concert. Perhaps it is just my misunderstanding, but I'm not sure what the differences are between pipes and filters and pipeline. It seems to me that one implements non-hardware pipelines through the use of pipes and filters, by using that groundwork to lay out the connections and the data handling between the linkages.

The Geometric Decomposition is interesting, and I would have loved to have a different example than one with pictures, as the 2D format makes them an obvious application. I would hope that in applications that need the additional efficiency is actually a complicated data set which would allow this, and although the example restricted itself to just the 2D data set I feel it is obvious that expansion into higher dimensional data sets would be a matter of defining cuts more closely and could be accomplished with a minimum of thought.