Code optimization refers to the practice of discovering at compile time information which will enhance a program’s runtime performance, whether that be reduced execution times, smaller code sizes or reduced energy usage during execution. This optimization could come in the form of reduced execution times, smaller code sizes or reduced processor energy use while running.
Compiler-specific transformations allow us to achieve this result, and one of the challenges lies in choosing which set and order of transformations to apply.
1. Use the Right Data Types
Coding optimization begins with choosing an appropriate data type. Selecting one with accurate representation of your data can reduce conversions, and speed up performance – for instance storing whole numbers as integers instead of strings will help reduce conversions, while using float calculations over string comparisons can save both memory and CPU cycles.
Platform independent code optimization techniques involve shortening instruction paths, using more efficient algorithms and eliminating unneeded instructions, in addition to memory and CPU cache optimizations. These are used at the source code level and can significantly enhance performance on various platforms by decreasing execution times or improving hardware efficiency when running programs.
Avoiding unnecessary iteration of loops is another essential component of code optimization, and should be performed at every level of compilation – source, intermediate and target levels alike. Doing this will reduce CPU cycles spent per loop cycle as well as memory references needed per cycle and should drastically cut costs associated with loop iterations. This form of optimization can be performed across these levels for maximum effectiveness.
2. Minimize Function Calls
Function calls incur both financial and non-financial costs; inefficiencies also stem from their need to recompute values when new inputs (whether arguments or returns) are provided; unnecessary arithmetic operations and redundant calculations may need to be performed repeatedly in order to produce their expected outputs.
Utilizing appropriate code optimization techniques can help reduce these costs, making your application run faster and more efficiently. But as with any technique, their use should be utilized responsibly: premature optimization often backfires by further complicating and slowing execution, possibly introducing bugs into software systems.
First step to optimizing code should always include using a flame graph as a profiler, to identify areas of your program that consume most time and select suitable optimization solutions for those sections of code.
This can be accomplished at either the source code or intermediate code level, and must involve transforming code to efficient target code. These transformations involve minimizing memory usage and CPU/output operations without altering the original source code – in other words they must remain transparent – hence we refer to this type of optimization as compile-time optimization. At this stage transformations include local and global optimization techniques like dead code elimination or inlining.
3. Use Caching
Modern processors and operating systems often store data at the system level in cache memory for rapid access, which helps reduce loads on various components and speed up frequent access cases. Caching shouldn’t be the starting point when developing performance oriented software; rather, build something quick right from the beginning to establish a benchmark baseline and ensure any intended performance increases from caching are actually realized.
Example: If a program reads an article via web browser, the app typically retrieves it from a server, processes it and displays it back to the user. But, if that article is frequently accessed, storing its result locally may make more sense as this will minimize load on server and speed up accessing.
As computer hardware becomes faster and development languages advance, the temptation is great to optimize code at every opportunity – but remembering this could actually slow it down or even over-optimize! Optimization requires careful consideration of goals of software projects, architecture structures and potential side effects from changes you are making before optimizing is made.
4. Use Inline Functions
Inline functions eliminate function call overhead, but may increase program size and cause memory thrashing if used in large numbers.
Make Your Function Inline When making a function inline, the compiler rewrites its code at compile time and inserts it directly into its calling code. This can speed up execution as no swapping of memory addresses is needed when copying arguments onto stack. However, making inline functions can make them harder to read and debug.
Inline functions are particularly useful in functions that are frequently called and/or have shorter execution times than switching times; additionally, inlining functions can reduce overall program size while improving portability to different platforms.
Static inline functions cannot contain modifiable static variables or references to other static variables/functions (unless using preprocessor tricks). The inline directive serves more as a hint than a requirement; the compiler can choose to ignore it and treat the function normally.
Utilizing the inline directive to request inline expansion is generally seen as premature optimization, since compilers can only optimize what they know about a function’s control flow. While this typically poses no problems, using it could impede loop unrolling and other optimizations that depend on knowing its structure.