Global Optimization

Global optimization has two goals. The first one is to put your code in a form where it is easy to do local optimiztions. For example, if you have a large procedure that performs several slow, complex calculations, you might see if you can break parts of that procedure into their own functions where the values can be precomputed or memoized.

Stateless functions (functions that only operate on the parameters that were passed to them - i.e. no globals or system calls) are the easiest type of functions to optimize in a computer. The more stateless parts of your program you have, the more opportunities you have to optimize. In the e-commerce situation I wrote about above, the computer had to find all of the associated parts for specific inventory items. This required about 12 database calls, and in the worst case took about 20 seconds. However, the goal of this program was to be interactive, and a long wait would destroy that goal. However, I knew that these inventory configurations do not change. Therefore, I converted the database calls into their own functions, which were stateless. I was then able to memoize the functions. At the beginning of each day, the function results were cleared in case anyone had changed them, and several inventory items were automatically preloaded. From then on during the day, the first time someone accessed an inventory item, it would take the 20 seconds it did beforehand, but afterwards it would take less than a second, because the database results had been memoized.

Global optimization usually often involves achieving the following properties in your functions:

Parallelization

• Parallelization means that your algorithm can effectively be split among multiple processes. For example, pregnancy is not very parallelizable because no matter how many women you have, it still takes nine months. However, building a car is parallelizable because you can have one worker working on the engine while another one is working on the interior. Usually, applications have a limit to how parallelizable they are. The more parallelizable your application is, the better it can take advantage of multiprocessor and clustered computer configurations.

Statelessness

• As we've discussed, stateless functions and programs are those that rely entirely on the data explicitly passed to them for functioning. Most processes are not entirely stateless, but they can be within limits. In my e-commerce example, the function wasn't entirely stateless, but it was within the confines of a single day. Therefore, I optimized it as if it were a stateless function, but made allowances for changes at night. Two great benefits resulting from statelessness is that most stateless functions are parallelizable and often benefit from memoization.

Global optimization takes quite a bit of practice to know what works and what doesn't. Deciding how to tackle optimization problems in code involves looking at all the issues, and knowing that fixing some issues may cause others.