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A system can generally be made optimal not in absolute terms but only with respect to a given quality metric, which may be in contrast with other possible metrics. As a result, the optimized system will typically only be optimal in one application or for one audience. One might reduce the amount of time that a program takes to perform some task at the price of making it consume more memory. In an application where memory space is at a premium, one might deliberately choose a slower algorithm in order to use less memory.

Automated andmanual optimization

Optimizing a whole system is usually undertaken by programmers because it is too complex for automated optimizers. In this situation, programmers or system administrators explicitly change code so that the overall system performs better. Although it can produce better efficiency, it is far more expensive than automated optimizations. Since many parameters influence the program’s performance, the optimization space is large. Meta-heuristics and machine learning are used to address the complexity of program optimization.

When to optimize

Optimization can reduce readability and add code that is used only to improve performance. This
may complicate programs or systems, making them harder to maintain and debug. As a result,
optimization or performance tuning is often performed at the end of the development stage.


Levels of optimization

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Design level

At the highest level, the design may be optimized to make the best use of the available resources, given goals, constraints, and expected use and load. The architectural design of a system overwhelmingly affects its performance.

Algorithms and data structures

Given an overall design, a good choice of efficient algorithms and data structures and an efficient implementation of these algorithms and data structures come next. After design, the choice of algorithms and data structures affects efficiency more than any other aspect of the program.

Source code level

Beyond general algorithms and their implementation on an abstract machine, concrete source code-level choices can make a significant difference. This depends on the source language, the target machine language, and the compiler and can be both difficult to understand or predict and change over time; this is a key place where understanding compilers and machine code can improve performance.

Build level

Between the source and compile levels, directives and build flags can be used to tune performance options in the source code and compiler, respectively, such as using preprocessor defines to disable unneeded software features, optimizing for specific processor models or hardware capabilities, or predicting branching, for instance.

Assembly level

At the lowest level, writing code using an assembly language designed for a particular hardware platform can produce the most efficient and compact code if the programmer takes advantage of the full repertoire of machine instructions. For this reason, many operating systems used on embedded systems have traditionally been written in assembler code.

Run time

Just-in-time compilers can produce customized machine code based on run-time data at the cost of compilation overhead. This technique dates to the earliest regular expression engines and has become widespread with Java HotSpot and V8 for JavaScript. In some cases, adaptive optimization may be able to perform run-time optimization beyond the capabilities of static compilers by dynamically adjusting parameters according to the actual input or other factors.


Technology optimization helps support the ongoing success of the core business, reduces costs, speeds up or automates processes, and boosts productivity. Information technology optimization is important for organizations of all sizes and in all stages of growth.

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