Optimization means different things to different people. In some situations, you might simply want your code to run as fast as possible. However, if you're writing code for an embedded system, you might prefer to optimize for code density to reduce your application's memory footprint.

Often these optimization constraints work against each other. For example, loop-unrolling is an optimization technique that can improve performance but this optimization comes at the expense of increased code size. The first step in optimization is deciding what you want to optimize.

Performance analysis tools

A common phrase in software optimization is "you can only optimize what you can measure".

This means that to improve the performance of your code, you must be able to accurately measure performance so that you can analyze where bottlenecks are occurring, make optimizations, and measure the improvements.

Coding best practices

How you write your source code can affect the efficiency of the executable code produced by the compiler. For example, loop counters that decrement to zero are generally more efficient than loop counters that increment to an arbitrary value, because the compiler can use a single instruction (SUBS) to decrement and compare to zero. Writing code that is more efficient delivers not only higher levels of performance, but can also be crucial in conserving battery life. If you can get a job done faster, in fewer cycles, you can turn off the power for longer periods.

  • Coding considerations describes programming practices and techniques to increase the portability, efficiency and robustness of your C and C++ source code.
  • Writing optimized code shows how you can use various options, pragmas, attributes, and coding techniques to make best use of the optimization capabilities of Arm Compiler.
  • Using inline assembly to improve code efficiency is a tutorial that shows how you can write optimized assembly language routines to improve performance.
  • The Cortex-A Programmer's Guide contains an whole chapter (Chapter 17) which discusses how to optimize code to run on Arm processors.

Compiler optimization

The compiler provides many different options for optimizing the code it produces. For example:

  • Vectorization enables the use of the NEON Single Instruction Multiple Data (SIMD) instructions that allow parallel processing of data.
  • Link Time Optimization (LTO) increases the number of optimization opportunities by analyzing source code from different modules together.
  • Function inlining can improve performance by reducing the overhead of repeated function calls.

These optimization techniques can be individually controlled using options supplied to the compiler and linker.