Code Optimization is a process that transforms source code to generate more efficient target code. It increases program speed and reduces the compilation time.

Modern hardware and compilers are so efficient that many hoped-for performance improvements fail to materialize. Sometimes a change makes the software better for one operation at the cost of making other operations less efficient.

1. Eliminate Unnecessary Computations

Optimizers can reduce the amount of time it takes for a program to run by removing unnecessary computations. They typically do this by rewriting sections of code that consume large amounts of CPU or memory. This is accomplished by using special utilities known as profilers. Programmers often have an intuitive sense of which parts of the program take the most resources, but the best way to be sure is to use a profiler.

A large part of a program’s execution time is spent in loops, which are notoriously difficult to optimize without sacrificing functionality. However, loop optimizations such as constant folding, induction analysis and reducing strength reduction can help reduce the overall compile time.

Ultimately, the goal of code optimization is to make the software run faster so that it produces results expected by users more quickly and accurately. Whether this means a smoother video game experience or a more responsive utility application, optimized code helps to ensure that the end user can get the most out of a given system. However, rewriting sections of code to improve performance can also introduce bugs that decrease maintainability.

2. Cache Data

Caching is one of the most common methods used to improve performance. It improves the speed of access to data by reducing the number of operations needed to retrieve it from the database. This can be done at either the application or operating system level. However, caching at the application or operating system level requires a complex set of algorithms and optimizations to deliver improvements. In addition, advances in hardware frequently negate the intended benefits of such optimizations.

At the application level, this can be done using write-around or read-through cache strategies. A write-around strategy stores data in the cache when it is written, whereas a read-through cache stores the data after being retrieved from the database. This avoids the expensive operations of reading and writing data back and forth to the database. However, a cache is limited in size and must periodically remove records to make room for new data. A least-recently-used (LRU) strategy is often employed to determine which records should be removed. This may require multiple cache reads, slowing performance. It also reduces the flexibility of the cache, since it is unable to serve different data sets simultaneously.

3. Avoid Round-About Optimizations

It’s important to remember that code optimization is generally a CPU and memory-intensive process. This can impact both runtime performance and compiler execution time. Moreover, it is often impossible to determine at compile time whether a given improvement will have sufficient benefit to outweigh the cost of the operation.

The primary goal of code optimization is to make the compiled program smaller, more portable, or more efficient. This can be achieved by reducing the size of the code, reducing memory usage, or reducing the number of input/output operations. Other goals can be to improve the responsiveness of the software, or reducing the energy used to run it.

Code optimization should be carried out after the architecture is set and algorithms or data structures have been defined, but before writing actual code. This will avoid NIHS, or Not Invented Here Syndrome, and minimize development time. The first step to identifying code that needs optimizing is profiling. This is essential, as even experienced engineers can incorrectly guess what needs changing. Once the profile has been performed, and a decision made about what to change, the changes should be carefully considered and thoroughly tested, including any effects on future development.

4. Avoid Function Call Overhead

Functions are great as a concept for making code more readable, but they come with a cost. Each time a function is called, it takes a small amount of overhead to save and restore the context, pass arguments, etc. On a micro level, this may add up to a few milliseconds per call. However, if the function is called many times, this can become a significant portion of the overall runtime.

Compilers work hard to optimize function calls by using techniques like inlining, constant substitution, smarter register usage and reducing function-to-caller dependencies through a process known as interprocedural optimization. However, these are only as effective as the algorithms being used by the programmer.

Programmers should never try to optimize a program without first using a profiler or performance analyzer to find out where the bottlenecks are. Optimizing an unimportant section of code can do little good and may actually harm performance. In some cases, this can even lead to duplication of code in order to avoid calling a function or method. This is called premature optimization and can have a negative impact on readability and maintainability of the software.

5. Avoid Inline Functions

Code optimization reduces the execution time of a program and can lead to a more responsive application. However, excessive use of inline functions can have the opposite effect and slow down a program.

Inline functions are functions that are inserted into the call site by replacing the function call with the function body itself. This can eliminate the overhead of creating a function object and jumping to a new memory location. Inline functions are useful for frequently used utility functions and in performance-critical code.

The inline keyword tells the compiler to replace the function call with the function body. However, the compiler is not obligated to perform this optimization.

If you use the inline keyword too often, it will increase the size of your header file and decrease the flexibility of your code. In addition, if your functions are too large for the CPU cache, they may cause more cache misses, leading to thrashing of memory and decreased performance. To avoid this, make sure to only use inline functions when it makes sense for the overall program design.