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Beyond Optimization: What is program super-optimization and how to make your code reach the extreme?
In the field of information technology, "program optimization" is not just a term, it is an art that involves optimizing all aspects of a software system to maximize efficiency, resource utilization, and relative performance. ideal state. The purpose of program optimization is to make a program perform better in terms of running speed, memory usage, etc., and even achieve lower power consumption in some cases.
Although the word "optimization" has the same etymology as "optimization", in fact it is rare to actually achieve an optimal system.
Often, an optimized system will not be optimal in an absolute sense, but will be optimized based on specific quality measures. This means that in different application scenarios, the required optimization methods may be different. For example, if memory consumption is increased in order to increase program execution speed, this may not be completely applicable to every situation.
Optimization levels
The optimization process can occur at multiple levels, from the design level to more specific algorithm and data structure choices, and finally to the source code level. Different levels of optimization have different impacts on the finished product. Usually, the higher the level of optimization, the consequences of the changes will be more significant and difficult to adjust later in the project.
Performance is part of a program's specifications: if the program is slow, it is not fit for purpose.
Optimization at the design level may involve making best use of available resources, but efficiency is also critical when choosing algorithms and data structures. If a design is network latency bound, you can choose to reduce data requests and avoid multiple round trips.
Algorithms and data structures
Efficient algorithm and data structure selection can significantly affect the overall efficiency of the program. Especially in programming, data structures are often more difficult to change than member functions. Therefore, when choosing a specific algorithm, one should focus on ensuring that its time complexity is within a reasonable range, such as constant O(1) or logarithmic O(log n). A small change, such as using "fast path" optimization techniques, often results in significant performance improvements.
Source code level optimization
At the specific source code level, certain choices can also cause significant performance differences. For example, in early C compilers, while(1) performed slightly slower than for(;;) when entering a conditional jump, because the former needed to evaluate the condition. But today's compiler event optimization capabilities have improved a lot.
Compilation level optimization
Programs automatically generated using an optimizing compiler can often guarantee a certain degree of optimization. Today's compiler load level choices can also bring performance improvements to your programs. However, in addition to the above efforts, as the technical threshold has become higher, modern compilers have evolved to be able to handle relatively complex codes and perform optimizations.
Runtime optimization
At runtime, some compilers generate custom machine code based on the current data, which demonstrates the advantages of just-in-time compilation technology. Such technologies are able to adjust based on real-world inputs, enabling a blend of flexibility and dynamic optimization. As a result, performance will improve depending on the runtime environment.
When to optimize
Although optimization can improve code execution efficiency, it sometimes also increases the difficulty of maintenance, so optimization is usually performed at the end of the development phase. As Donald Knuth said: "We should forget small efficiencies and don't think too much 97% of the time; but in the critical 3% of the time, we should not miss opportunities." This is a warning to developers during the optimization process , must balance the structure and performance of the code.
"Advance optimization is the root of all evil."
In this context, today's programmers, faced with the balance between technological progress and actual efficiency, how should they choose the best path to achieve the most ideal state of their program codes?
