cpu-basics

Explain foundational CPU kernel concepts and patterns for AKG operators.

258|48|Updated Jun 22, 2020
One-click install
npx skills add https://github.com/mindspore-ai/akg --skill cpu-basics-mindspore-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: cpu-basics
Source: https://github.com/mindspore-ai/akg/tree/main/akg_agents/python/akg_agents/op/resources/skills/cpp/guides/cpu-basics
Command: npx skills add https://github.com/mindspore-ai/akg --skill cpu-basics-mindspore-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides foundational concepts and standard patterns for CPU-based AKG kernels, guiding engineers to structure and implement operators efficiently.

Core Features & Use Cases

  • Kernel concepts: Kernel, tensor handling, memory management, and type safety.
  • Standard five-step kernel structure: sequence from input validation to output creation and type restoration.
  • KernelBench templates and inline C++ guidelines for rapid CPU operator development.

Quick Start

Create a minimal CPU kernel scaffold following the standard five-step pattern to implement your first operator.

Frequently Asked Questions about cpu-basics

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

FAQPage Schema
What is the standard structure for writing a CPU kernel in AKG?

The standard CPU kernel structure in AKG follows a five-step sequence progressing from input validation to output creation and type restoration, ensuring contiguous memory handling and type safety.

How do I implement a CPU operator with contiguous memory handling?

To implement a CPU operator with contiguous memory handling, apply the standard five-step AKG kernel pattern that sequences input validation, tensor processing, memory management, output creation, and type restoration.

Can I use AKG kernel patterns for both x86_64 and aarch64 architectures?

Yes, AKG CPU kernel concepts and patterns are applicable across both x86_64 and aarch64 architectures for performance-focused operator implementations and kernel design.

What's the best way to start developing CPU-backed operator implementations?

The best way to start developing CPU-backed operators is by creating a minimal kernel scaffold using KernelBench templates and inline C++ guidelines following the foundational five-step pattern.

Does AKG support type safety and tensor handling for CPU kernels?

Yes, AKG supports type safety and tensor handling for CPU kernels by incorporating these concepts directly into the foundational kernel structure and standard implementation patterns.

Why do I need a five-step pattern for CPU kernel design?

You need the five-step pattern for CPU kernel design to ensure a simple, consistent startup guide that properly sequences input validation, tensor handling, memory management, output creation, and type restoration.