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exp-simd-vectorization

Optimize .NET 8+ math loops with SIMD intrinsics and TensorPrimitives.

Updated May 28, 2026
Search Tags:#performance optimization#vectorization#.net#simd#math operations
One-click install
npx skills add https://github.com/ojrojas/AgentsInstructions --skill exp-simd-vectorization-ojrojas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exp-simd-vectorization
Source: https://github.com/ojrojas/AgentsInstructions/tree/main/.claude/skills/dotnet-experimental/skills/exp-simd-vectorization
Command: npx skills add https://github.com/ojrojas/AgentsInstructions --skill exp-simd-vectorization-ojrojas

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires System.Numerics.Tensors, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of optimizing hot-path scalar loops in .NET 8+ applications with SIMD intrinsics, improving performance by leveraging Vector128/Vector256/Vector512 SIMD operations.

Core Features & Use Cases

  • SIMD Optimization: Utilizes SIMD intrinsics for high-performance math operations on contiguous numeric arrays.
  • TensorPrimitives API: Provides a set of APIs for vectorized computations, reducing the need for manual SIMD code.
  • Manual SIMD Implementation: Offers guidance on writing manual SIMD code using Vector128/Vector256/Vector512 intrinsics for specific cases.
  • Use Case: Ideal for developers looking to optimize critical sections of their .NET applications that involve heavy mathematical computations.

Quick Start

Analyze your .NET code for scalar loops that can be optimized with SIMD and use the provided guidelines to apply SIMD intrinsics or TensorPrimitives API calls.

Frequently Asked Questions about exp-simd-vectorization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize math operations in .NET 8 using SIMD?▼

SIMD optimization in .NET 8 accelerates mathematical computations by processing multiple data points simultaneously using Vector128, Vector256, or Vector512 intrinsics on contiguous numeric arrays. This Skill provides guidelines to replace scalar loops with these vectorized operations.

What is the TensorPrimitives API for .NET vectorized computations?▼

The TensorPrimitives API provides built-in vectorized computations for contiguous numeric arrays in .NET 8, reducing the need for manual SIMD code. It handles common math operations internally to deliver high-performance results without manual intrinsic management.

When should I use manual SIMD intrinsics instead of TensorPrimitives in .NET?▼

Use manual SIMD intrinsics like Vector128 or Vector256 for specific cases where the TensorPrimitives API lacks coverage. Manual implementation provides finer control over critical hot-path math operations on contiguous numeric arrays.

Does .NET 8 support hardware-accelerated math operations on arrays?▼

Yes, .NET 8 supports hardware-accelerated math operations on contiguous numeric arrays through SIMD intrinsics and the TensorPrimitives API. This allows developers to optimize hot-path scalar loops by leveraging CPU vector capabilities.

What do I need to know to implement SIMD vectorization in .NET applications?▼

Implementing SIMD vectorization in .NET requires knowledge of the .NET framework, familiarity with SIMD intrinsics, and the ability to analyze scalar loops in hot-path sections. The Skill targets .NET 8+ environments for contiguous numeric arrays.

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