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exp-simd-vectorization

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Speed up hot-path .NET code with portable SIMD.

Software Engineering#performance#benchmarking#dotnet#vectorization#simd#intrinsics#tensorprimitives
Authorsayedihashimi
Version1.0.0
Installs0

System Documentation

What problem does it solve?

High-performance .NET code often suffers from hot-path loops that do scalar work; this skill provides a guided approach to replace those loops with cross-platform SIMD intrinsics or TensorPrimitives to unlock vectorized execution.

Core Features & Use Cases

  • Portable SIMD intrinsics: Vector128/Vector256/Vector512 patterns for common loop operations.
  • TensorPrimitives integration: replace scalar loops with high-level API calls for reductions, transforms, and fused operations.
  • Automatic fallback: scalar path remains for environments without SIMD support and for edge cases.
  • Use cases: data processing, numeric computations, byte-range validation, and character counting in performance-sensitive paths.

Quick Start

Start by identifying a hot-path loop and mapping it to a SIMD vectorization plan, then implement with corresponding TensorPrimitives calls, followed by benchmarking to measure speedups.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

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Please help me install this Skill:
Name: exp-simd-vectorization
Download link: https://github.com/sayedihashimi/copilot-skill-eval/archive/main.zip#exp-simd-vectorization

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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