The code is found on the site, near the end of the article. To run it, I made a Makefile. The code downloaded and borrowed from the site are stored as text files, so I renamed them as .cpp and .h files within the Linux environment. Here are the files for ease of access: [[File:main.cpp.txt]] | [[File:image.h.txt]] | [[File:image.cpp.txt]]Also, here is the Makefile:

The Ignoring the real time spent, the majority of time is spent in the equals operator function and the class constructor, most likely because the image is constantly being manipulated, read from, and being copied to and from temporary storage for ease of use and object safety. Other than the basic functions (like read/write), it looks like the rotate and enlarge functions take a larger amount of time, which could mean that, if they were to be parallelized, it could positively affect the run time. My discernment of the big-O notation for the rotate function is O(n^2) which shows a quadratic growth rate, whereas the enlarge function had a notation of O(n^3) or greater. The reason for the rotate function having a longer run-time could be due to the fact that I enlarged the image before rotating it, but the notations don't lie. Personally, I'd say that this application is not the best for parallelization because of its simplicity in handling the images, but I can definitely see how one or more of the functions in the program can be parallelized. Some of the issues posed in making the program parallel is centered upon the image needing to be accessible to every other function, and, considering that the image is being processed, it would be constantly modified and read from. I simple terms, I think that, if multiple threads were running to quicken the program, the computation of the image could lead to errors in processing resulting in a corrupted image, distortions, and things of the sort. I may be wrong in this thought, but, to my knowledge, not being to avoid such issues makes this program somewhat difficult to safely parallelize.