Migrating SIMD (Single Instruction, Multiple Data) code from x86 extensions to Arm extensions is a key task for software developers aiming to optimize performance on Arm platforms.
Understanding the mapping from x86 instruction sets such as SSE, AVX, and AMX to Arm’s NEON, SVE, and SME extensions is essential for achieving portability and high performance. This Learning Path provides an overview to help you design a migration plan in which you can leverage Arm features such as scalable vector lengths and advanced matrix operations to adapt your code effectively.
Vectorization is a key optimization strategy where one instruction processes multiple data elements simultaneously. It drives performance in High-Performance Computing (HPC), AI and ML, signal processing, and data analytics.
Both x86 and Arm processors offer rich SIMD capabilities, but they differ in philosophy and design. The x86 architecture provides fixed-width vector units of 128, 256, and 512 bits. The Arm architecture offers fixed-width vectors for NEON and scalable vectors for SVE and SME, ranging from 128 to 2048 bits.
If you are migrating SIMD software to Arm, understanding these differences will help you write portable, high-performance code.
This section provides some more information about the Arm vector and matrix extensions and shows you when to use each, how they map from SSE/AVX/AMX, and what changes in your programming model (predication, gather/scatter, tiles, streaming mode).
NEON is a 128-bit SIMD extension available across Armv8-A cores, including Neoverse and mobile. It is well suited to multimedia, DSP, and packet processing. Conceptually, NEON is closest to x86 SSE and AVX used in 128-bit mode, making it the primary target when migrating many SSE workloads. Compiler auto-vectorization to NEON is mature, reducing the need for manual intrinsics.
SVE introduces a revolutionary approach to SIMD with its vector-length agnostic (VLA) design. Registers in SVE can range from 128 to 2048 bits, with the exact width determined by the hardware implementation in multiples of 128 bits. This flexibility allows the same binary to run efficiently across different hardware generations. SVE also features advanced capabilities like per-element predication, which eliminates branch divergence, and native support for gather/scatter operations, enabling efficient handling of irregular memory accesses. While SVE is ideal for high-performance computing (HPC) and future-proof portability, developers must adapt to its unique programming model, which differs significantly from fixed-width SIMD paradigms. SVE is most similar to AVX-512 on x86, offering greater portability and scalability.
SME accelerates matrix multiplication and is similar in intent to AMX. Unlike AMX, which often uses dot-product oriented operations, SME employs outer-product oriented operations. SME integrates with SVE, using scalable tiles and a streaming mode to optimize performance. It is well suited to AI training and inference, as well as dense linear algebra in HPC applications.
Here is a brief overview of the x86 families you’ll likely port from: SSE (128-bit), AVX/AVX-512 (256/512-bit with masking), and AMX (tile-based matrix compute). Use this to identify feature equivalents before mapping kernels to NEON, SVE/SVE2, or SME on Arm.
The SSE instruction set provides 128-bit XMM registers and supports both integer and floating-point SIMD operations. Despite being an older technology, SSE remains a baseline for many libraries due to its widespread adoption.
However, its fixed-width design can constrain throughput compared with newer extensions like AVX. When migrating code from SSE to Arm, developers will find that SSE maps well to Arm NEON, enabling a relatively straightforward transition.
AVX provides 256-bit YMM registers, and AVX-512 adds 512-bit ZMM registers. Features include FMA, per-lane masking in AVX-512, and VEX or EVEX encodings. When moving AVX workloads to Arm, 128-bit paths often translate to NEON, while algorithms that scale with vector width are good candidates for SVE. Because SVE is vector-length agnostic, refactor for predication and scalable loops to maintain portability and performance.
AMX accelerates matrix operations with tile registers configured using a tile palette. It suits AI workloads such as GEMM and convolutions. When migrating AMX kernels to Arm, target SME. While both target matrix compute, AMX commonly expresses dot products, while SME focuses on outer products, so porting often entails algorithmic adjustments.
Use these side-by-side tables to pick the right Arm target and plan refactors. They compare register width, predication/masking, gather/scatter, key operations, typical workloads, and limitations for SSE ↔ NEON, AVX/AVX-512 ↔ SVE/SVE2, and AMX ↔ SME.
| Feature | SSE | NEON |
|---|---|---|
| Register width | 128-bit (XMM) | 128-bit (Q) |
| Vector length model | Fixed 128 bits | Fixed 128 bits |
| Predication or masking | Minimal, no dedicated mask registers | No dedicated mask registers; use bitwise selects and conditionals |
| Gather/scatter | No native gather/scatter; gather in AVX2 and scatter in AVX-512 | No native gather/scatter; emulate in software |
| Instruction set scope | Arithmetic, logical, shuffle, convert, basic SIMD | Arithmetic, logical, shuffle, saturating ops; cryptography via Armv8 Cryptography Extensions (AES and SHA) |
| Floating-point support | Single and double precision | Single and double precision |
| Typical applications | Legacy SIMD, general vector arithmetic | Multimedia, DSP, cryptography, embedded compute |
| Extensibility | Extended by AVX, AVX2, and AVX-512 | Fixed at 128-bit; scalable vectors provided by SVE as a separate extension |
| Programming model | Intrinsics in C/C++; assembly for hotspots | Intrinsics widely used; inline assembly less common |
| Feature | x86: AVX or AVX-512 | Arm: SVE or SVE2 |
|---|---|---|
| Register width | Fixed: 256-bit YMM, 512-bit ZMM | Scalable: 128 to 2048 bits in 128-bit steps |
| Vector length model | Fixed; often multiple code paths for different widths | Vector-length agnostic; same binary adapts to hardware width |
| Predication or masking | Mask registers in AVX-512 | Rich predication via predicate registers |
| Gather or scatter | Gather in AVX2 and scatter in AVX-512 | Native gather and scatter across widths |
| Key operations | Wide SIMD, FMA, conflict detection, advanced masking | Wide SIMD, FMA, predication, gather or scatter, reductions, bit manipulation |
| Best suited for | HPC, AI and ML, scientific computing, analytics | HPC, AI and ML, scientific computing, cloud and scalable workloads |
| Limitations | Power and thermal headroom under heavy 512-bit use; ecosystem complexity | Requires VLA programming style; SVE or SVE2 hardware availability varies by platform |
SVE2 extends SVE with richer integer and DSP capabilities for general-purpose and media workloads.
| Feature | x86: AMX | Arm: SME |
|---|---|---|
| Register model | Tile registers configured via a palette; fixed per type limits | Scalable matrix tiles integrated with SVE; implementation-dependent dimensions |
| Vector length model | Fixed tile geometry per configuration | Scales with SVE vector length and streaming mode |
| Predication or masking | Predication not inherent to tiles | Predication via SVE predicate registers |
| Gather or scatter | Not provided in AMX tiles; handled elsewhere | Via SVE integration with gather or scatter |
| Key operations | Dot-product oriented GEMM and convolution | Outer-product matrix multiply; streaming mode for dense linear algebra |
| Best suited for | AI and ML training and inference, GEMM and convolution kernels | AI and ML training and inference, scientific and HPC dense linear algebra |
| Limitations | Hardware and software availability limited to specific CPUs | Emerging hardware support; compiler and library support evolving |
The most significant changes when porting include moving from fixed-width SIMD to vector-length-agnostic loop structures, replacing mask-register control with predicate-driven control, and adjusting memory access patterns and compiler flags. Review this section first to minimize rework and preserve portable performance.
x86 SIMD (SSE, AVX, and AVX-512) uses fixed widths of 128, 256, or 512 bits. This often requires multiple code paths or dispatch strategies. Arm NEON is also fixed at 128-bit and is a familiar baseline. SVE and SME introduce vector-length agnostic execution from 128 to 2048 bits so the same binary scales across implementations.
x86 intrinsics are extensive, and AVX-512 adds masks and lane controls that increase complexity. NEON intrinsics look familiar to SSE developers. SVE and SME use predication and scalable loops. Prefer auto-vectorization and VLA-friendly patterns over heavy hand-written intrinsics when portability matters.
AMX provides fixed-geometry tile compute optimized for dot products. SME extends Arm’s scalable model with outer-product math, scalable tiles, and streaming mode. Both AMX and SME are currently available on a limited set of platforms.
Migrating from x86 SIMD to Arm entails adopting Arm’s scalable and predicated programming model with SVE and SME for forward-portable performance, while continuing to use NEON for fixed-width SIMD similar to SSE.
Several libraries help translate or abstract SIMD intrinsics to speed up migration. Coverage varies, and some features have no direct analogue.
Here are some of the tools available and their key features:
For more on cross-platform intrinsics, see the Learning Path Porting architecture-specific intrinsics .