vectorization

name	vectorization
description	Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including Vector<T>, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.
license	MIT

.NET SIMD vectorization

Produce a portable optimization that preserves the scalar contract, remains memory-safe at every length, and earns its complexity with measured results. Read the official SIMD and hardware-intrinsics guidance first and follow its comprehensive implementation templates. In particular, use its self-contained per-width dispatch, dedicated small-input handling, loop, and remainder shapes rather than reducing them to a chain of width checks. This skill supplies the decision rules and validation checks to apply while changing real code.

Inputs and prerequisites

Discover these from the repository before asking the user:

Input	Required	What to establish
Scalar implementation and tests	Yes	Existing contract, representative call sites, and supported overlap
Target frameworks and platforms	Yes	Available SIMD APIs and architectures that must behave consistently
Build and test workflow	Yes	The repository's normal commands and how to launch separate test processes
Representative workload or benchmark	For optimization	Typical input sizes and the baseline to beat

Do not add a package merely because an API exists there. First check the target framework and the project's existing dependency/versioning policy.

Core rules

1. Use the highest-level API that matches the contract, then stop. Span<T> and string operations, TensorPrimitives, and tensor types already accelerate many operations. LINQ reductions such as Sum, Min, Max, and Average can also accelerate when the source exposes its underlying span. Verify empty-input and floating-point behavior rather than assuming similarly named operations are interchangeable. Once an existing API preserves the contract, use it instead of continuing into handwritten SIMD. Before writing an explicit loop, name the framework APIs considered and why none applies. Fixed-shape System.Numerics types remain appropriate for graphics and similar domains.
2. Start new explicit SIMD loops with Vector128<T>. It is accelerated across the broadest hardware set. Add wider fixed-width paths only when measurements justify them.
3. Keep platforms consistent. Prefer cross-platform operations on the fixed-width vector types; they lower to the appropriate target instructions. For example, (vector & mask) == Vector128<byte>.Zero becomes ptest on x86/x64. Use architecture-specific intrinsics only for a measured gap, guard them with IsSupported, and retain equivalent portable or scalar behavior.
4. Read IsHardwareAccelerated, IsSupported, and Count directly. The JIT treats them as constants, so caching them adds no value and obscures which branches disappear.
5. Prefer operators where they are clear. Parenthesize expressions that mix bitwise and comparison operators so precedence is explicit.

If the task is review-only, do not rewrite the code. Report correctness and memory-safety defects before performance opportunities.

Authoring checklist

• Contract: identify behavior for empty and short inputs, overlap, overflow, NaN, signed zero, ordering, and exceptions before changing the implementation.
• Framework gate: inspect the target framework and existing package references, then compile or probe the highest-level candidate API with the required edge cases. A small contract adapter, such as preserving special empty-input behavior, does not justify reimplementing the operation. If the API preserves the contract, use it and stop; do not claim it is unavailable without checking.
• Structure: for new explicit SIMD, implement Vector128<T> and scalar first. Only after measurements justify wider paths, check Vector512<T>, then Vector256<T>, optional Vector<T>, Vector128<T>, and finally scalar. Omit paths the implementation does not need. Each outer fixed-width guard checks only its IsHardwareAccelerated property and, for generic element types, IsSupported. Inside that block, run the width-specific helper when the input has at least Count elements; otherwise run a dedicated small-input helper, then return. Do not put the length check in the outer guard and fall through to repeat dispatch at narrower widths. Keeping each supported-width block self-contained lets the JIT remove unsupported blocks and avoids redundant work on common small inputs.
• Loads and stores: prefer span-based Vector128.Create(span) and CopyTo; the JIT keeps them efficient and they require no pinning or reference arithmetic. Unsafe loads and stores are largely unnecessary. When a path genuinely must walk a buffer by managed reference, use the element-offset LoadUnsafe(ref T, nuint) and StoreUnsafe overloads rather than pointers or manually advanced references.
• Empty inputs: in a reference-based path, obtain the starting reference with MemoryMarshal.GetReference(span) or MemoryMarshal.GetArrayDataReference(array), not by indexing element 0.
• Unsupported element types: the fixed-width vectors support primitive numeric element types, not char or bool. Reinterpret with MemoryMarshal.Cast or As<TFrom, TTo>; reinterpretation changes only the type, not the bits. Keep Boolean data as 0 or 1 and characters as valid UTF-16, normalizing results before storing when necessary.

The official guidance contains the complete dispatch, small-input, unrolling, and remainder templates; use those for the full implementation. The following excerpt illustrates only the inner safe Vector128<T> loop for an in-place elementwise transform, after its self-contained dispatch block has established at least one full vector. Transform represents the operation being implemented:

Span<int> tail = data.Slice(data.Length - Vector128<int>.Count);
Vector128<int> end = Vector128.Create<int>(tail);
Span<int> remaining = data;

while (remaining.Length >= Vector128<int>.Count)
{
    Vector128<int> values = Vector128.Create<int>(remaining);
    Transform(values).CopyTo(remaining);
    remaining = remaining.Slice(Vector128<int>.Count);
}

if (!remaining.IsEmpty)
{
    Transform(end).CopyTo(tail);
}

The early end load preserves original values before overlapping stores. For a read-only reduction, load the same final span after the main loop and use ConditionalSelect to replace already-processed lanes with the operation's identity. Do not substitute LoadUnsafe/StoreUnsafe or a scalar epilogue merely to avoid span bounds checks.

Testing checklist

• Compare the optimized implementation with the scalar contract across boundary lengths, randomized values, empty inputs, supported overlap, and numeric edge cases. Cover every implemented width and the scalar path with inputs both large enough and too small to benefit.
• Exercise every implemented width and the scalar fallback in separate processes. On x86/x64 CoreCLR, DOTNET_EnableAVX2=0 disables AVX2 and DOTNET_EnableHWIntrinsic=0 disables hardware intrinsics. Use the repository's normal test command and do not change these process-wide settings inside a unit test. These settings do not change code already compiled as ReadyToRun or ahead of time, so confirm the target code is JIT-compiled when using them to force a path.
• For unsafe loads and stores, use guard-page or equivalent boundary tests when available. Put the inaccessible page after the buffer for forward iteration and before it for backwards iteration, and include nonmultiple lengths. An ordinary array allocation does not reliably expose an out-of-bounds read.

Benchmarking

Use BenchmarkDotNet to measure representative small and large inputs before keeping the added complexity. Compare scalar, Vector128<T>, and each wider implemented path in the same run. Small inputs can be slower because setup dominates, and speedups are rarely the theoretical vector-width multiple because memory throughput, alignment, and latency still apply. Report throughput or time with noise context and, when relevant, generated code size or instruction counts. Control allocation alignment for stable measurements or randomize it to observe the distribution. A wider vector is not automatically faster.

If the project cannot target the required framework, run the relevant architecture, or execute the fallback configuration, state exactly which path remains unverified. Do not claim success from a default-hardware test alone.

Completion contract

• Authoring: leave the scalar contract covered by tests; identify the framework or SIMD layer selected; report measurements for the representative workload; name any architecture or fallback path that could not be exercised.
• Review: report only concrete findings, ordered by correctness, memory safety, portability, tests, then performance evidence. If none remain, say so directly.
• Do not call an optimization complete when it only builds, only passes on the current machine, or has no comparison against the scalar baseline.

Review checklist

Review in this order:

1. Scalar-contract equivalence, including signed zero, NaN, overflow, and relevant endianness
2. Reuse of an existing accelerated framework API
3. Tail correctness for idempotent versus non-idempotent work
4. Memory safety, unsigned offset arithmetic, empty inputs, and overlapping buffers
5. Portable dispatch and behaviorally equivalent fallbacks
6. Tests that force each width and the scalar path
7. Benchmarks that justify explicit SIMD and additional widths