Program optimization is modifying a computer program to execute faster, consume fewer resources, or function more efficiently. Optimization can be done automatically by some programming language compilers, on purpose by using an optimization program, or manually by programmers who step through source code and try to make specific improvements. In general, program optimization is done with a particular goal in mind because there are very few general optimizations that can be made to a program that does not reduce the optimized state of another part of the program, which means that a program can usually be optimized for speed or resource usage, but not both.
Most computer users blame unoptimized software programs on computer hardware or hardware configuration. Fortunately, good programmers continue to be concerned with software performance and strive to write optimized code. A 20-year-old computer, for example, can still run a modern GNU/Linux distribution flawlessly. Any software project must write optimized code to achieve good software quality and stability. Writing optimized code is a good software engineering practice that is heavily required for the codebases of large tech companies.
IT process optimization now has a lot of help from technologies like cloud computing, collaborative software, and, in some cases, Big Data and Machine Learning. The goal of IT process optimization in this context is far more predictive than simply providing technical support. Even in emergencies, the use and provisioning of systems and software must meet the company’s needs. This requires IT managers to understand the organization’s strategic objectives and how they add value to its solutions to external and internal customers, ensuring they are always satisfied.
Optimization solvers can now be embedded in a wide range of applications used by thousands and thousands of leading businesses today to tackle complex problems and make optimal decisions due to pivotal technological breakthroughs. The impact and applicability of optimization technologies across various industries continue to grow in today’s business world, with more and more companies using optimization tools daily.
Optimization is broadly classified into two types: machine-independent and machine-dependent.
Machine Independent code optimization attempts to improve the efficiency of intermediate code by transforming a section of code that does not involve hardware components such as CPU registers or absolute memory locations. In general, it optimizes code by removing redundancies, reducing the number of lines of code, removing unnecessary code, and decreasing the frequency of repeated code. As a result, it can be used on any processor, regardless of machine specifications.
Machine-dependent optimization occurs after the target code has been generated and transformed to fit the target machine architecture. It uses CPU registers and may use absolute rather than relative memory references. Machine-dependent optimizers work hard to make the most of the memory hierarchy.
The fact that performance is a continuous variable distinguishes bug fixing from performance tuning. Either a feature is coded, or it is not. Either a bug is present or not. However, performance can be abysmal, extremely good, or somewhere in the middle. Optimization is also an iterative process in which a new slowest part of the program appears each time the slowest part of the program is improved. Finally, optimization is an experimental science that necessitates a more scientific mindset than other coding tasks. To be successful at optimization, it is necessary to observe behavior, form testable hypotheses based on these observations, and conduct experiments that result in measurements that support or refute the theories.
We make decisions and choose between solutions that may appear equivalent at first glance while writing code. Later, it is usually discovered that some choices result in a more efficient program than others, resulting in a search for best coding practices and optimization techniques. We begin to see the entire development process as an optimization problem. Whatever approach we use, there is one rule that every code optimization effort must follow: we must always carry out the optimization in a way that does not change the meaning of the code.