exp-simd-vectorization

仓库创建 2026年2月4日最近提交 18 小时前SkillHot 收录 20 天前

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覆盖多种矢量指令宽度与常见算法，适合性能敏感代码库。

这个 Skill 做什么

为 .NET 8+ 的热路径标量循环使用 Vector128/256/512 与 TensorPrimitives 进行矢量化优化。

帮你把 .NET 8+ 中的性能热点标量循环用 SIMD 指令加速，支持 Vector128/Vector256/Vector512，也可以用 TensorPrimitives 把手写循环替成一次性 API 调用。适合对吞吐量或延迟敏感的场景，比如字节范围校验、字符计数、大量按位运算、跨类型批量转换和浮点密集计算时使用。特别之处是兼顾底层跨平台 SIMD 原语和高阶 TensorPrimitives 接口，能在不大幅牺牲可读性的前提下把热路径矢量化。

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Optimizes hot-path scalar loops in .NET 8+ with cross-platform Vector128/Vector256/Vector512 SIMD intrinsics, or replaces manual math loops with single TensorPrimitives API calls. Covers byte-range validation, character counting, bulk bitwise ops, cross-type conversion, fused multi-array computations, and float/double math operations.

开发编程SIMD性能优化.NET通用

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# SIMD Vectorization

## Decision Gate
1. **Check `Span<T>` and `MemoryExtensions` first.** If the operation can be expressed using built-in `Span<T>` methods (e.g., `Contains`, `IndexOf`, `CopyTo`, `SequenceEqual`) or `MemoryExtensions`, use them — no additional dependency is needed and the runtime already vectorizes many of these internally.
2. **Check for TensorPrimitives next.** If one or more TensorPrimitives methods cover the operation → use them. If the `.csproj` does NOT already reference `System.Numerics.Tensors`, **add the package**, for example: `<PackageReference Include="System.Numerics.Tensors" />` (or use the versioning approach already used by your solution). Then replace the

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