triton-cuda-debugging

Diagnose Triton-CUDA kernel issues with a structured debugging checklist.

6|1|Updated Apr 19, 2026
One-click install
npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill triton-cuda-debugging
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: triton-cuda-debugging
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/triton-cuda/guides/triton-cuda-debugging
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill triton-cuda-debugging

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Triton CUDA 调试排查清单和常见错误速查表,提供系统化的诊断方法,帮助开发者在遇到编译、运行时、数值稳定性与性能问题时快速定位原因并验证实现正确性。

Core Features & Use Cases

  • 完整的排查清单:覆盖内存访问、控制流、网格/块配置、并发、数值稳定性与性能等方面的诊断。
  • 实用的技巧与用例:提供常见错误的诊断思路和修复要点,便于快速验证改动。
  • Use Case: 在调试 Triton CUDA 内核时,本指南可作为日常排错和提交前自查的标准流程。

Quick Start

走进排查流程,按清单逐步检查并验证潜在问题,快速定位并修正 Triton-CUDA 内核中的错误。

Frequently Asked Questions about triton-cuda-debugging

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

FAQPage Schema
How do I debug Triton CUDA kernel compile-time and runtime errors?

Debug Triton CUDA kernel errors by applying a structured diagnostic checklist covering compile-time failures, runtime crashes, memory access violations, and control flow issues to isolate the root cause.

What is the best way to diagnose Triton CUDA kernel performance bottlenecks and precision issues?

Diagnose Triton CUDA kernel performance bottlenecks and precision issues by evaluating grid and block configurations, atomic usage, and numerical stability through a methodical debugging workflow to verify implementation correctness.

Why does my Triton CUDA kernel fail during memory access or grid block configuration?

Triton CUDA kernel memory access and grid block configuration failures stem from out-of-bounds indexing or mismatched dimensions, requiring a systematic checklist to verify block sizes, grid layouts, and control flow logic.

Can I use a standard checklist to verify Triton CUDA kernel atomic usage and numerical stability?

Verify Triton CUDA kernel atomic usage and numerical stability by following a methodical debugging workflow that enforces diagnostic best practices for concurrent operations and precision validation.