# Use SIMD.info to port SIMD intrinsics across Arm architectures

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/)
- [Overview](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo/)
- [SIMD.info Features](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/simdinfo-description/)
- [Example Program](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/simdinfo-example1/)
- [Porting Process](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont/)
- [Code Verification](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting/)
- [Intrinsics without Equivalents](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/simdinfo-example2/)
- [Conclusion](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/conclusion/)
- [Next Steps](https://learn.arm.com/learning-paths/cross-platform/simd-info-demo/_next-steps/)

## About this Learning Path

| Skill level:      | Advanced          |
|-------------------|-------------------|
| Reading time:     | 30 min            |
| Last updated:     | 04 Aug 2026       |

| Authors:                                         |
|--------------------------------------------------|
| Georgios Mermigkis, VectorCamp [GitHub](https://github.com/gMerm) [LinkedIn](https://linkedin.com/in/georgios-mermigkis) [VectorCamp](https://vectorcamp.gr/) |
| Konstantinos Margaritis, VectorCamp [GitHub](https://github.com/markos) [LinkedIn](https://linkedin.com/in/konstantinosmargaritis) [Twitter](https://twitter.com/@freevec1) [VectorCamp](https://vectorcamp.gr/) |

| Arm IP:                                           |
|---------------------------------------------------|
| [Cortex-A](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors) [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors) |

| Tags:                                |
|--------------------------------------|
| [Performance and Architecture](https://learn.arm.com/tag/performance-and-architecture) [Linux](https://learn.arm.com/tag/linux) [GCC](https://learn.arm.com/tag/gcc) [Clang](https://learn.arm.com/tag/clang) [Rust](https://learn.arm.com/tag/rust) [Runbook](https://learn.arm.com/tag/runbook) |

### Who is this for?

This Learning Path is for software developers who are interested in porting SIMD code across Arm platforms.

### What will you learn?

After completing this Learning Path, you'll be able to perform the following tasks:

- Describe how to use SIMD.info's tools and features, such as navigation, search, and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures to improve code portability.

### Prerequisites

Before starting, you'll need the following prerequisites:

- A basic understanding of SIMD.
- Access to an Arm platform with a SIMD-supported engine, installed with recent versions of a C compiler such as Clang or GCC.

### Summary

You’ll use SIMD.info to port SIMD intrinsics from a concise SSE4.2 C example to Arm Neon/ASIMD. You’ll search and compare intrinsic pages, and map comparison, addition, multiplication, and square-root operations. Then, you’ll adapt vector initialization and storage. You’ll replace the SSE calls with Neon constructs and compare the results with the original behavior, prioritizing correctness over performance.

### Frequently asked questions

<details>
<summary>How do I find the Arm Neon equivalent for an SSE intrinsic in SIMD.info?</summary>
Search for the SSE intrinsic name, such as `"_mm_cmpgt_ps"`, and open its page. Review **Purpose**, **Result**, and **Example**, then use the comparison features to find the suggested Neon counterpart.
</details>

<details>
<summary>How do I decide between multiple Neon options shown for the same SSE intrinsic?</summary>
Compare the **Purpose** and **Result** descriptions, then review the examples to match your code’s semantics. Choose the option that matches your data types and intended operation sequence.
</details>

<details>
<summary>What should I change when adapting vector initialization and storage for Neon?</summary>
Replace SSE macro-style initialization, such as `"_mm_set_ps"`, with Neon’s brace initialization. Update stores for Neon’s memory operations and preserve the original element ordering.
</details>

<details>
<summary>Which operations from the example should I map when porting the code?</summary>
Map `"_mm_cmpgt_ps"`, `"_mm_add_ps"`, `"_mm_mul_ps"`, and `"_mm_sqrt_ps"` to their Neon equivalents in SIMD.info. These intrinsics cover the example’s comparison, addition, multiplication, and square-root operations.
</details>

<details>
<summary>What should I do if there is no one-to-one Neon replacement listed?</summary>
Use the **Purpose** and **Result** details to compose equivalent behavior from multiple Neon operations. Implement the closest semantic match and compare the output with the SSE baseline.
</details>