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triton-cuda-grid-config

Configure CUDA kernel launch parameters for Triton kernels.

258|48|Updated Jun 22, 2020
Search Tags:#cuda#grid#triton#kernel-tuning#grid-stride#gpu-occupancy
One-click install
npx skills add https://github.com/mindspore-ai/akg --skill triton-cuda-grid-config-mindspore-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: triton-cuda-grid-config
Source: https://github.com/mindspore-ai/akg/tree/main/akg_agents/python/akg_agents/op/resources/skills/triton-cuda/guides/triton-cuda-grid-config
Command: npx skills add https://github.com/mindspore-ai/akg --skill triton-cuda-grid-config-mindspore-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Grid/Block configuration strategies help developers determine CUDA kernel launch parameters, optimize SM occupancy, and efficiently handle large-shape inputs for Triton-based kernels.

Core Features & Use Cases

  • Defines grid dimensional conventions (1D, 2D, 3D) and grid-stride looping patterns.
  • Supports 1D element-wise kernels and 2D/3D batch/grid configurations for large data shapes.
  • Provides best practices for Triton CUDA kernel tuning and performance guidance.

Quick Start

Provide BLOCK_SIZE and grid dimensions, then launch the Triton CUDA kernel with those settings.

Frequently Asked Questions about triton-cuda-grid-config

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

FAQPage Schema
How do I configure CUDA grid dimensions for faster Triton kernels?▼

Configure CUDA grid dimensions for Triton kernels by defining 1D, 2D, or 3D grid shapes alongside BLOCK_SIZE to maximize SM occupancy and safely handle large-scale inputs.

What is a grid-stride loop and when do I need it for large GPU tensors?▼

A grid-stride loop allows a CUDA grid to process large-shape inputs beyond the hardware grid limit by iterating over data in blocks, ensuring safe grid sizing and preventing under-utilization.

How do I optimize GPU occupancy and prevent under-utilization in Triton?▼

Optimize GPU occupancy in Triton by tuning kernel launch parameters to configure safe grid sizing, ensuring the grid shape matches large data inputs to prevent over- or under-utilization.

Does this grid configuration approach support 2D and 3D batch configurations?▼

Yes, the grid configuration approach supports 2D and 3D batch configurations, allowing developers to define multi-dimensional grid shapes for large-scale data processing in Triton kernels.

Can I use autotune support when defining CUDA grid shapes?▼

Yes, optional autotune support is available when configuring CUDA grid shapes, allowing dynamic selection of optimal kernel launch parameters to maximize GPU utilization.

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