Optimize your code with vectorization using Intel C++ Compiler

Optimize your code with vectorization using Intel C++ Compiler

Table of Contents

1. Introduction
2. Background Information
3. Creating and Initializing Arrays
4. Looping through the Arrays
5. Adding the Arrays
6. Compiling the Code
   1. Setting Up the Compiler Variables
   2. Compiling the Code
   3. Enabling Vectorization Report
   4. Checking Vectorization Report
   5. Analyzing the Assembler Output
7. Conclusion

Introduction

In this article, we're going to explore how to optimize the compiler to generate vectorized code. We'll take a look at the assembler code to ensure that it is indeed vectorized. We'll walk through the process step by step, starting from creating and initializing arrays to compiling the code and analyzing the output. By the end of this article, you'll have a clear understanding of how to leverage vectorization to optimize your code for performance.

Background Information

Before we dive into the technical details, let's first understand what vectorization is and why it is important. Vectorization is a technique that allows for the simultaneous execution of multiple operations on a set of data using vector registers. It leverages the Parallel processing power of modern CPUs to perform computations faster. By vectorizing code, we can increase efficiency and improve overall performance.

Creating and Initializing Arrays

To demonstrate the process of vectorization, we'll start by creating and initializing three arrays. Each array will hold 128 floats, and we'll populate them with test data. Array A will hold values divided by 10, array B will hold values divided by 20, and array C will be initialized to 0. Initializing array C is not necessary in this case, as the compiler automatically adds zeros. However, it's good programming practice to initialize variables explicitly.

Looping through the Arrays

Next, we'll loop through the arrays and perform vectorized addition on the corresponding elements. In this example, we'll focus on addition for simplicity. However, vectorization supports various operations. For each iteration, we'll add the elements of arrays A and B and store the result in array C. This is where the vectorization takes place, optimizing the computation for parallel execution.

Adding the Arrays

Once the loop completes, we'll print the contents of array C to verify the results. This step allows us to ensure that the vectorized addition was performed correctly.

Compiling the Code

Now, let's move on to compiling the code and examining the vectorization process in detail.

Setting Up the Compiler Variables

To begin, we'll set up the compiler variables using the Intel command-line compiler. By executing the appropriate scripts, we'll configure the compiler for Intel 64, both for the host and target systems. This step ensures that the compiler optimizes the code specifically for the Intel 64 architecture.

Compiling the Code

With the compiler variables set, we can now compile our code. The Intel command-line compiler, ICL, is used to compile the C++ code. By specifying the file name, we can compile the code successfully.

Enabling Vectorization Report

To gain insights into the vectorization process, we'll enable the vectorization report. This report provides detailed information about which sections of the code were vectorized and which ones were not. By analyzing this report, we can ensure that the code is being optimized properly.

Checking Vectorization Report

After compiling the code with vectorization report enabled, we'll check the report for any vectorized sections. By scrolling through the report, we can identify the lines of code that were successfully vectorized. For example, lines 11 and 17 in our code indicate successful vectorization.

Analyzing the Assembler Output

To understand the generated assembler code, we'll examine the assembler output. By adding the appropriate compiler option, we can generate an assembly file. Opening the assembly file, we can analyze the code line by line. The "V" in the assembly code indicates that vectorization was performed. We'll focus on line 18, where the vectorization occurs, and observe the packed single precision numbers being moved and added. This analysis confirms that the vectorization is targeting the AVX processor.

Conclusion

In this article, we explored the process of optimizing code for vectorization. We started by creating and initializing arrays, performed vectorized addition in a loop, and analyzed the assembler output to ensure successful vectorization. By leveraging vectorization, developers can achieve significant performance improvements in their code. Remember to enable the vectorization report and carefully analyze the output to ensure successful optimization.

【Highlights】

• Understanding the concept of vectorization and its importance in code optimization.
• Creating and initializing arrays for vectorized operations.
• Performing vectorized addition on arrays to leverage parallel processing power.
• Compiling the code using the Intel command-line compiler.
• Enabling the vectorization report to analyze and verify successful vectorization.
• Examining the assembler output to ensure that vectorization is targeting the desired processor.

【FAQ】

Q: What is vectorization? A: Vectorization is a technique that enables the parallel execution of multiple operations on a set of data using vector registers, leading to improved performance.

Q: How can I enable the vectorization report in the Intel command-line compiler? A: To enable the vectorization report, use the appropriate command-line option, such as "Kubek,report -".

Q: Does vectorization work only for addition operations? A: No, vectorization can be applied to various operations, including addition, subtraction, multiplication, and division, among others.

Q: How can I analyze the assembler output to confirm vectorization? A: By examining the assembly code generated by the compiler, you can identify the sections where vectorization was applied and observe the corresponding vectorized instructions.

Q: Can vectorization be applied to any type of code? A: Vectorization is most effective when applied to computationally intensive code with data-level parallelism. It may not yield significant improvements in code that lacks such characteristics.

【Resources】

• Intel Developer Zone