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As you can see if you select a lot of clusters the image will appear very similar to the original but if you select a small number of clusters most of the detail is gone.
==== Conclusion and Observations ====
While the EucideanDistance project shows great promise in being optimized and parallelized (will be upwards of 90% once redundant functions are merged), it suffers from the draw back of having limited test data. The test data required for the EucideanDistance project is a text based image based in 1s and 0s. Along with this, the 'image' file used is also rather unpresentable.
The seam carver project displays poor results in regards of parallelization and optimization. There is no real identifiable hotspot in any of the function calls.
The KmeansPlusPlus project shows to be somewhat promising in regards to parallelization and optimization. At first glance, there does not seem to be a exact hotspot as two functions take around 50% of the work. However, on close examination, one of the function take 50% but is only called once in one project execution vs the other at above 8 million. Optimizing and improving at least the one portion of this project would prove to be advantageous.
In conclusion, our findings indicate that KmeansPlusPlus would be the best project to continue onto assignment 2, with EucideanDistance as the back up.
