vectorization

name

vectorization

description

Guidance for writing and reviewing SIMD / hardware-intrinsics code in dotnet/runtime. USE FOR: vectorizing a scalar algorithm, writing or reviewing code that uses Vector128/Vector256/Vector512, Vector<T>, or the platform intrinsics in System.Runtime.Intrinsics.X86/Arm/Wasm, and validating remainder handling, load/store safety, and hardware-acceleration fallbacks. DO NOT USE FOR: general performance work unrelated to SIMD (use performance-benchmark), or non-vectorized code review (use code-review).

SIMD and vectorization in dotnet/runtime

The general, cross-cutting guidance for SIMD and hardware intrinsics lives in the official .NET documentation. Read it first and defer to it for anything not specific to this repo:

• Use SIMD and hardware intrinsics in .NET

The repo-specific nuance is in docs/coding-guidelines/vectorization-guidelines.md. This skill distills what to actually enforce when authoring or reviewing vectorized changes here.

Core rules

1. Reach for the highest-level API that already does the job. Span<T>/string methods, TensorPrimitives, and the tensor types already vectorize many operations. LINQ is often vectorized too — operators such as Sum, Max, Min, and Average accelerate when the source's underlying span can be extracted. Don't hand-roll what's already optimized and tested.
2. Start with Vector128<T>. It's the common denominator accelerated on the broadest hardware, and you don't need Vector256/Vector512 for a correct, portable implementation. Add wider widths and platform intrinsics only for a measured hot path.
3. Keep platforms consistent. Prefer the cross-platform APIs on Vector128/Vector256; they lower to the optimal instruction per target (for example (vector & mask) == Vector128<byte>.Zero becomes ptest on x86/x64). Only drop to System.Runtime.Intrinsics.X86/Arm/Wasm when a specific instruction measurably beats the portable form, and guard it with the class's IsSupported.
4. Read IsHardwareAccelerated and Count directly; don't cache them to locals. Both are JIT-time constants, so caching buys nothing, and a local obscures that constant-ness — read them at each use so the branches you don't take are eliminated.
5. Prefer operators over named methods (+, &, <<) for readability, but mind precedence: a & b == c parses as a & (b == c), so parenthesize when mixing them.

Authoring checklist

• Structure: widest-supported width first, working down to a scalar fallback for small inputs and non-accelerated hardware. Guard each width with Vector128.IsHardwareAccelerated and Vector128<T>.IsSupported (the latter matters in generic code), then compare length against Count.
• Loads and stores: prefer the span-based Vector128.Create(span) / CopyTo — the JIT keeps them efficient and they need no pinning or reference arithmetic. The unsafe load/store variants are largely no longer needed; when you genuinely must walk a buffer by managed reference, use the LoadUnsafe(ref T, nuint elementOffset) / StoreUnsafe element-offset overloads rather than raw pointer or ref arithmetic.
• Empty buffers: get the starting reference from MemoryMarshal.GetReference (or GetArrayDataReference for arrays), not ref span[0].
• Reinterpreting unsupported types: Vector128<T> supports the primitive numerics, not char or bool. Reinterpret via MemoryMarshal.Cast (a span) or the vector's As<TFrom, TTo> — for example char → ushort. Reinterpretation changes only the type, not the bits, so keeping the data well-formed is on you (a bool stays 0/1, a char a valid UTF-16 code unit); normalize any out-of-range result before writing it back.
• Offset arithmetic is unsigned (nuint). Always check the buffer length before computing an offset like buffer.Length - Vector128<int>.Count; if the buffer is smaller than one vector that subtraction underflows to a huge value.
• Always handle the remainder. Reprocess the last full vector's worth of elements, overlapping what the loop already did. For an idempotent operation (a search) fold the overlap in directly; for a non-idempotent operation (a sum) mask the overlap to the operation's identity with ConditionalSelect first.
• Watch backwards iteration. Never let an intermediate ref point outside its buffer, even transiently — a GC that runs at that moment won't update it, producing a GC hole. See the LastIndexOf case study (#73768 / fix).
• Account for buffer overlap when loading from one buffer and storing into another.

Testing checklist

• Cover every code path: the Vector256 path, the Vector128 path, and the scalar path — each with inputs both large enough and too small to benefit.
• Toggle acceleration via environment variables (can't be done at the unit-test level): run the suite with no overrides, with DOTNET_EnableAVX2=0 (disables Vector256), and with DOTNET_EnableHWIntrinsic=0 (disables all intrinsics down to the software fallback). Build the affected library and run its test project per the build/test workflow in the build-and-test skill, with the relevant DOTNET_Enable* variable set in the environment.
• Guard against out-of-bounds reads with BoundedMemory. BoundedMemory.Allocate<T>(count) places a no-access page immediately after the buffer (use PoisonPagePlacement.Before for backwards-iterating algorithms), so on most targets a read past the end faults with an access violation instead of silently succeeding. It falls back to an unprotected allocation on Browser/WASI and .NET Framework, so don't rely on the guard there. Always include lengths that aren't an exact multiple of the vector width.

Benchmarking

Vectorization adds complexity, so measure that it pays off before keeping it. Use BenchmarkDotNet and the same DOTNET_Enable* variables to compare scalar / Vector128 / Vector256 in one run. Keep in mind: larger inputs benefit more (small buffers can be slower due to setup), speedups are rarely the theoretical multiple (memory throughput, alignment, and latency all factor in), and randomized allocation alignment adds noise — allocate aligned memory or enable BenchmarkDotNet's randomization for stable/observable results. For non-trivial changes, use the performance-benchmark skill.

Review checklist

When reviewing a vectorized change, verify in priority order:

1. Correctness first — does it match the scalar contract, including signed-zero/NaN/overflow and endianness (BitConverter.IsLittleEndian) edge cases? Verify any claim about existing behavior.
2. Reuse vs duplication — should this use an existing higher-level API, helper, or shared loop instead of an Nth hand-rolled copy?
3. Remainder handling — is the tail covered, and is the idempotent-vs-masked choice correct?
4. Memory safety — no unguarded nuint underflow, no ref straying outside its buffer, empty buffers handled, overlap considered.
5. Cross-platform consistency — does it diverge from other architectures without justification? Prefer the portable API unless a per-platform intrinsic is justified by numbers.
6. Tests — all paths covered (including AV testing via BoundedMemory) and run under the acceleration-toggle env vars? Ask for the missing test rather than just rejecting.
7. Perf claims — backed by concrete numbers (codegen bytes, throughput, ns with noise context), not assertions. A wider vector is not automatically faster.