Code Optimization is a process of transforming source code to improve its performance. It can help software programs run faster and use less memory.

It also helps improve portability between different platforms and reduces power usage at runtime. Code optimization typically involves a combination of transformations. Each transformation targets a specific category of inefficiency.

Speed

Code optimization improves the program’s speed by reducing execution time and the number of resources used. It can also reduce the size of the program, making it easier to distribute and deploy. However, if done incorrectly, it can introduce bugs into the code or decrease performance.

Often, the most critical code fragments for overall code efficiency are identified with special tools called profilers. These identify the routines that take the most CPU time and can be optimized to increase performance without affecting functionality. In particular, interrupt service routines, high-priority tasks and calculations with real-time deadlines are likely candidates.

Many loops can be optimized by removing or substituting expensive expressions for cheaper ones. For example, multiplication (x * 2) consumes more CPU cycles and memory than division (x / 2); a simple swap can save both. Other methods include transforming loop invariant code to reduce the number of iterations and eliminating redundant or unnecessary steps within the loop.

Passing values by value versus passing references can be an easy way to improve the performance of a function without affecting its code. Similarly, by reducing the number of conversions between data types, you can save on both object creation and copying.

Efficiency

Code optimization is the process of modifying code to make it more efficient. This can be achieved by using different data types, removing unnecessary operations, and reducing memory usage. Efficient code can improve a program’s speed, functionality, and readability.

Some optimizations have a trade-off: they may make the software better for some operations but worse for others. This can be a problem when the software is used in a way that takes advantage of the best features of the hardware, such as when it is run on multi-core processors.

Using profiling tools and benchmarking to identify bottlenecks in the code helps focus optimization efforts on the most critical parts of the program. This can help ensure that the performance gains are actually made. In addition, writing automated tests helps catch bugs and ensures that changes to the code don’t introduce new problems.

Keeping the code clean and concise also helps improve its efficiency. Avoid duplicating code and make sure that functions have a clear purpose. This can reduce the number of function calls and reduce the amount of data that needs to be passed between functions. Using the smallest data type possible can also improve performance by minimizing conversions. Finally, it is important to use caching techniques to reduce memory usage.

Memory

In some cases, optimizing code can reduce memory usage, which can free up valuable storage space for other tasks. This is important in embedded situations where memory use can impact system performance and imposes hardware limitations. However, this type of optimization must be balanced against readability, as it can introduce code that makes a program harder to understand or maintain.

Code optimization can occur at multiple levels in the compiling process. Regional optimization transforms basic blocks, such as function and procedure calls, while global optimizations target entire programs. These techniques include loop unrolling, super local value numbering, and interprocedural optimization. These methods may change the meaning of the code, but they can reduce the amount of work that is performed by the CPU.

Another way to optimize a program is to use the compiler’s whole-program optimization option, which delays most of the transformations until the linker stage. This allows the compiler to search the entire program, potentially discovering that variable abc is never used or changed and that a failing branch or jump can be eliminated.

In addition to making the program run faster, code optimization can reduce memory use, and sometimes improve software maintainability by eliminating unnecessary variables and data structures. In the case of memory-hungry systems, optimization can be a critical factor in avoiding thrashing, where the computer spends more time transferring data to and from RAM than it does accomplishing tasks.

Caching

Caching can reduce the number of read and write operations to slow storage devices or networks, such as disks, reducing the overall time required to perform a program’s tasks. The storing of temporary data in the cache can also free up system memory to allow more operations to be performed per second.

Caches can be implemented at the source or compile level with code optimization directives and build flags. These can be used to disable unneeded software features, optimize for processor models or hardware capabilities, predict branching and other performance factors, or reduce memory use.

In addition to saving application processing time, caching can also save on memory storage and energy use. In some cases, this can be substantial.

Because caches are limited in size, some records have to be removed when newer data is inserted to make room for the latest entries. The most common method is a least-recently-used (LRU) strategy, which removes the record whose last access occurred before any other recent entries in the cache.

While simple caches can be very effective in increasing application performance, they do have some significant limitations. One of the most significant is that they do not provide any context for the data being retrieved. Instead, a company’s sole reliance on caches results in a patchwork of siloed data sets, difficult to integrate and analyze.