kernel-tileir-optimization

Optimize Triton kernel autotune configurations for NVIDIA TileIR on Blackwell GPUs.

Updated May 23, 2026
One-click install
npx skills add https://github.com/yo-steven/skills-exploration-20260522 --skill kernel-tileir-optimization-yo-steven
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: kernel-tileir-optimization
Source: https://github.com/yo-steven/skills-exploration-20260522/tree/main/skills/TensorRT-LLM/kernel-tileir-optimization
Command: npx skills add https://github.com/yo-steven/skills-exploration-20260522 --skill kernel-tileir-optimization-yo-steven

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, triton, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Triton kernels often underperform on NVIDIA Blackwell GPUs unless their autotune configurations match TileIR backend expectations. This skill optimizes existing Triton kernels by focusing on TileIR-relevant tuning parameters (notably occupancy and dot-kernel TMA/2CTA patterns) while keeping correctness via compatibility checks.

Core Features & Use Cases

  • Kernel classification for targeted optimization: Detects dot-related, norm-like, element-wise, and reduction kernels to choose the right TileIR strategy.
  • TileIR-specific autotune config generation: Adds occupancy, num_ctas (2CTA for dot-related kernels), and num_stages guidance appropriate for TileIR.
  • TMA descriptor guidance for dot kernels: Provides required wiring patterns (including pre-hook updates) to avoid runtime errors or silent wrong results.
  • Validation and benchmarking workflow: Recommends ENABLE_TILE toggling to compare PTX vs TileIR performance and verify correctness.

Quick Start

Run the skill’s classification and apply TileIR optimizations to your kernel by executing a one-step command that classifies and rewrites autotune configurations: python scripts/classify_kernel.py --file kernel.py --apply-optimizations

Frequently Asked Questions about kernel-tileir-optimization

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

FAQPage Schema
How do I optimize Triton kernel performance on NVIDIA Blackwell GPUs?

To optimize Triton kernel performance on Blackwell GPUs, apply TileIR-specific autotune configurations for occupancy, num_ctas, and num_stages, then validate correctness using ENABLE_TILE compatibility mode toggling.

What is TileIR autotune strategy for Triton kernels?

TileIR autotune strategy selects backend-specific configurations for Triton kernels by classifying them as dot-related, norm-like, element-wise, or reduction, then applying appropriate occupancy, num_stages, and TMA/2CTA wiring parameters.

How do I set up TMA descriptors for dot-related Triton kernels?

TMA descriptors for dot-related Triton kernels require specific wiring patterns including pre-hook updates to avoid runtime errors or silent wrong results, using 2CTA configurations within the TileIR autotune strategy on Blackwell GPUs.

Do I need to rewrite my Triton kernel to use TileIR optimization?

No, TileIR optimization applies to existing Triton kernels by changing autotune configurations rather than rewriting kernel logic, targeting dot-related, norm-like, element-wise, and reduction kernels that need TileIR parameter updates.

How do I validate Triton kernel correctness when switching to TileIR?

Validate Triton kernel correctness by running with ENABLE_TILE=0 in compatibility mode to compare standard PTX output against TileIR performance, ensuring the optimized autotune configurations produce accurate results.

Can I use TileIR optimization for all types of Triton kernels?

TileIR optimization supports dot-related, norm-like, element-wise, and reduction kernels on Blackwell GPUs, but requires ENABLE_TILE gating and specific TMA/2CTA wiring for dot kernels, making it unsuitable for kernels needing logic rewrites.