Optimize SIMD code with vectorization-friendly data layout
Introduction
What exactly is data layout?
Improve data alignment
Increase complexity
Write hand optimized SIMD code
Structure of arrays
Migrate to the Scalable Vector Extension (SVE)
Next Steps
Optimize SIMD code with vectorization-friendly data layout
Who is this for?
This is an advanced topic for C/C++ developers who are interested in improving the performance of SIMD code.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Comprehend the importance of data layout when writing SIMD code
Prerequisites
Before starting, you will need the following:
- An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
You’ll learn how data layout affects SIMD vectorization on Arm. Starting with an Array-of-Structures model that stores x, y, and z values in 12-byte triplets, you’ll examine how strided access limits four-wide floating-point operations. You’ll add padding for four-element vectors, handle boundary checks, and write a hand-optimized Arm NEON version when auto-vectorization is insufficient. Then, you’ll compare a Structure-of-Arrays layout with contiguous component arrays and explore an SVE implementation using the simulation examples.
Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
If your structure stores 3-element vectors (x, y, and z), the 12-byte grouping creates strided access that can make 4-wide SIMD vectorization of 32-bit floats difficult. Use compiler output and the example programs to see how this layout affects vectorization.
Replace the three-element
vec3 fields with four-element vec4 fields and initialize the additional element as padding. The 16-byte layout makes four-wide operations easier for the compiler. The later Structure-of-Arrays example stores each component contiguously instead.Copy
simulation1.c to simulation2.c to add bounding-box logic, including the updated simulate_objects() function, the ctr4 structure, and the box constant. Then copy simulation2.c to simulation3.c for the hand-written NEON version. Create simulation4.c for the Structure-of-Arrays approach, or simulation4_sve.c for the SVE example.simulation3.c includes arm_neon.h and uses types such as float32x4_t to process data in 4-wide chunks. The math is expressed with NEON intrinsics, so vector operations are explicit in the source.The Structure-of-Arrays version stores each component, such as all x values, in its own contiguous array. This improves sequential access and makes 4-wide operations straightforward, reducing the penalties of interleaved fields.