Tutorial: Using Auto Vectorization with Intel® Fortran Compiler

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ID 758497
Date 4/11/2022
Public
Document Table of Contents
Document Table of Contents x
Tutorial: Using Auto Vectorization Overview
Tutorial: Using Auto Vectorization Overview x
Introduction to Auto Vectorization Tutorial: Linux* and macOS* Tutorial: Windows* Version Additional Exercises Notices and Disclaimers
Tutorial: Linux* and macOS* x
Preparing the Sample Application Establishing a Performance Baseline Generating a Vectorization Report Improving Performance by Aligning Data Improving Performance with Interprocedural Optimization
Tutorial: Windows* Version x
Preparing the Sample Application Establishing a Performance Baseline Generating a Vectorization Report Improving Performance by Aligning Data Improving Performance with Interprocedural Optimization
Tutorial: Using Auto Vectorization Overview

Introduction to Auto Vectorization

For the Intel® Compiler, vectorization is the unrolling of a loop combined with the generation of packed SIMD instructions. Because the packed instructions operate on more than one data element at a time, the loop can execute more efficiently. It is sometimes referred to as auto-vectorization to emphasize that the compiler automatically identifies and optimizes suitable loops on its own.

Intel® Advisor can assist with vectorization and show optimization report messages with your source code. See https://software.intel.com/content/www/us/en/develop/tools/advisor.html for details.

Vectorization may call library routines that can result in additional performance gain on Intel microprocessors than on non-Intel microprocessors. The vectorization can also be affected by certain options, such as m or x.

Vectorization is enabled with the compiler at optimization levels of O2 (default level) and higher for both Intel® Microprocessors and non-Intel® Microprocessors. Many loops are vectorized automatically, but in cases where this doesn't happen, you may be able to vectorize loops by making simple code modifications. In this tutorial, you will:

  • Establish a performance baseline

  • Generate a vectorization report

  • Improve performance by aligning data

  • Improve performance using Interprocedural Optimization

This tutorial is available in Linux*, macOS*, and Windows* versions.

Parent topic: Tutorial: Using Auto Vectorization Overview
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