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Choose CUDA Launch Configuration

Determine optimal CUDA launch configurations by analyzing resource usage and hardware limits.

54|7|Updated Apr 10, 2026
Search Tags:#cuda#occupancy#launch-configuration#block-size#gpu-kernels#grid-size#register-pressure
One-click install
npx skills add https://github.com/tensormux/kernel-skills --skill choose-cuda-launch-configuration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Choose CUDA Launch Configuration
Source: https://github.com/tensormux/kernel-skills/tree/main/skills/cuda/choose-launch-configuration
Command: npx skills add https://github.com/tensormux/kernel-skills --skill choose-cuda-launch-configuration

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill guides AI agents through selecting efficient CUDA thread block and grid dimensions to maximize occupancy while respecting register and shared memory constraints, tail effects, and cooperatively launched grids.

Core Features & Use Cases

  • Occupancy analysis: estimate theoretical and actual active warps per SM for candidate block sizes.
  • Resource-aware tuning: account for register usage, shared memory, and dynamic smem in planning launches.
  • Use Case: optimize a kernel with significant shared memory and registers by choosing block sizes that maximize occupancy and minimize tail effects, while considering cooperative launch constraints.

Quick Start

Start by profiling kernel resource usage, then select a block size and grid that maximize occupancy within hardware limits.

Frequently Asked Questions about Choose CUDA Launch Configuration

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

FAQPage Schema
How do I choose the best CUDA block size to maximize GPU kernel occupancy?▼

Estimate theoretical and actual active warps per SM for candidate block sizes by analyzing register pressure and shared memory constraints to maximize GPU kernel occupancy.

What is the best way to determine grid size when launching cooperative CUDA kernels?▼

Calculate optimal thread block dimensions that respect grid limits and cooperative launch constraints while validating active warps per SM to determine the grid size.

How does shared memory affect CUDA launch configuration and register pressure?▼

Dynamic and shared memory usage directly constrain active warps per SM. Account for shared memory and register pressure when tuning block sizes to prevent occupancy drops.

Can I validate my CUDA launch configuration using occupancy APIs?▼

Validate CUDA launch configurations by checking theoretical active blocks per multiprocessor using occupancy APIs like cudaOccupancyMaxActiveBlocksPerMultiprocessor to ensure maximum utilization.

Why does my CUDA kernel performance drop when using large block sizes?▼

Large CUDA block sizes can cause register pressure and shared memory limits, reducing active warps per SM. Tune block size to balance occupancy and minimize tail effects.

When should I consider tail effects during GPU kernel launch configuration?▼

Consider tail effects when problem sizes are not multiples of the block size. Minimize tail effects by choosing block dimensions that evenly divide the workload while maximizing occupancy.

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