9.3 Performance improvements
A key feature of any big data algorithm is locating a way to execute some kind of a divide-and-conquer strategy. This is true of functional programming design as well as imperative design.
Here are three options to speed up this processing:
We can try to use parallelism to do more of the calculations concurrently. On a four-core processor, the time can be cut to approximately 25 percent. This reduces the time to 8 minutes for Manhattan distances.
We can see if caching intermediate results will reduce the amount of redundant calculation. The question arises of how many colors are the same and how many colors are unique.
We can look for a radical change in the algorithm.
We’ll combine the last two points by computing all the possible comparisons between source colors and target colors. In this case, as in many other contexts, we can easily enumerate the entire mapping of pixels and colors. If colors are repeated, we avoid doing redundant calculations...
