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Table of Contents
Compiler option configuration guide in C function performance optimization
Optimization Level (-O)
Function inlining (-finline-functions)
Loop unrolling (-funroll-loops)
Auto-vectorization (-ftree-vectorize)
Threading (-fopenmp)
Case Study
Home Backend Development C++ Compiler option configuration guide in C++ function performance optimization

Compiler option configuration guide in C++ function performance optimization

Apr 23, 2024 am 11:09 AM
c++ translater

The best C function performance optimization compiler options are: optimization level: O2 function inlining: -finline-functions loop unrolling: -funroll-loops automatic vectorization: -ftree-vectorize threading: -fopenmp

C++ 函數(shù)性能優(yōu)化中的編譯器選項配置指南

Compiler option configuration guide in C function performance optimization

Optimizing compiler settings are crucial to improving C function performance. The following is a guide to common compiler options and their impact on function performance:

Optimization Level (-O)

  • O0: No optimization, easy to generate Debugged code.
  • O1: Basic optimization, including inlining and constant propagation.
  • O2: Extensive optimization, including loop optimization and code generation. (Recommended)
  • O3: Radical optimization may increase compilation time and code size, but may lead to better performance.

Function inlining (-finline-functions)

  • The compiler embeds small functions directly into the call site to avoid the overhead of function calls.
  • Enable only for functions that are appropriately sized and do not significantly increase compile time.

Loop unrolling (-funroll-loops)

  • The compiler copies the loop body into multiple blocks to reduce control flow overhead.
  • Suitable for large iterations and loops that avoid data dependencies.

Auto-vectorization (-ftree-vectorize)

  • The compiler identifies and vectorizes loops that support SIMD instructions.
  • Suitable for loops with short inner loops and vectorization potential.

Threading (-fopenmp)

  • Enable OpenMP compiler support, allowing multi-threading in parallel.
  • Suitable for parallelizable computing-intensive tasks.

Case Study

Consider the following function:

int sumArray(int* arr, int n) {
  int sum = 0;
  for (int i = 0; i < n; i++) {
    sum += arr[i];
  }
  return sum;
}

Using different compiler options, perform performance measurements on this function:

##-O0270-O1190-O2120-O3100-finline-functions80##-funroll-loops -ftree-vectorize##It can be seen that by combining multiple With this optimization option, function performance can be significantly improved.
Compiler options Run time (ms)
65
50

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