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computation-analysis

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Diagnose and optimize Ascend NPU computation.

Software Engineering#optimization#analysis#profiling#bottlenecks#Ascend NPU#CANN
AuthorFeRhodium
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill helps identify computation-intensive operators and assesses CANN support to optimize Ascend NPU performance.

Core Features & Use Cases

  • Identify computation-heavy operators and their locations within model code (e.g., matmul, convolution, attention).
  • Assess CANN operator library support status and identify CPU fallback risks.
  • Propose practical optimization opportunities with torch_npu, such as AMP usage, operator fusion, and data layout improvements.
  • Provide a structured profiling approach and actionable guidance for performance validation.
  • Use Case: analyze a PyTorch model to locate bottlenecks and generate a profiling plan for Ascend NPU optimization.

Quick Start

Use the computation-analysis skill to identify computation-heavy operators in your Ascend NPU model and generate a profiling plan.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: computation-analysis
Download link: https://github.com/FeRhodium/ascend-migration/archive/main.zip#computation-analysis

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