Branch-and-Bound, Graphical Models, Structured Grids

It never fails that Murphy will make an appearance right when I am getting close to wrapping things up. We have bandwidth limitations on internet usage in Australia. Do you remember using dial-up modems? That's about what it feels like when you use all of your allocated bandwidth for the month. Unfortunately, it made it nearly impossible to access the UIUC wiki page to get the papers for the final reading assignment. Here is my lackluster, yet final, submission for this reading blog...

Branch-and-Bound

The branch & bound pattern is fairly well written. Although the use of the data flow diagram did assist in explaining the pattern, it is unfortunate there were no actual code examples to demonstrate the branch and bound operations of branching, evaluation, bounding, and pruning. It was beneficial we had previously discussed the task parallelism, data parallelism, shared queue, and data distribution as they were used to demonstrate the parallelization approaches. I do appreciate the authors not getting to deep into the noise of the technical aspects and kept the pattern fairly high level for a well-rounded audience.


Graphical Models

Does this pattern have anything to do with parallelism? Ok, I understand the idea of modeling the probability distribution, but I do comprehend how this is applied to everyday use. What is the true benefit here? The example, if you call it an example, provided no real insight into what the pattern was about. I wanted to read and comment on the pattern before listening to the recorded discussion. Now, I am interested to hear the follow-on discussion after reading my classmates' comments.


Structured Grid Computational Pattern

This pattern was co-authored by an UIUC undergrad alum who went on to UC-Berkely to complete her PhD. The idea of representing a multi-dimension problem in the form of structured grid implemented as an n-dimensional array is very interesting. I do not have any background or experience in this area. As the array is broken into chunks for parallel processing, the tasks cannot be too fine-grained resulting in additional overhead and reducing efficiency of parallelism. The Geometric Decomposition pattern is handy in this situation.

I am interested in looking at additional information related to "Ghost" nodes, double-buffering, and adaptive mesh refinement.