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Refine vectorization guidance and evals

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Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
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Tanner Gooding
2026-08-25 08:49:06 -07:00
parent 021c7df89a
commit 9f20ab0ecd
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plugins/dotnet-advanced/skills/vectorization/SKILL.md
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@@ -36,11 +36,13 @@ project's existing dependency/versioning policy.
## Core rules
1. **Use the highest-level API that matches the contract.** `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. Fixed-shape `System.Numerics` types remain appropriate for
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.