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GPU Acceleration Skill

Configure NVIDIA GPU acceleration for WSClean and CuPy in the DSA-110 imaging pipeline.

Updated Aug 27, 2026
Search Tags:#docker#gpu#imaging#cuda#acceleration#dsa-110#wsclean
One-click install
npx skills add https://github.com/dsa110/dsa110-contimg --skill gpu-acceleration-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: GPU Acceleration Skill
Source: https://github.com/dsa110/dsa110-contimg/tree/main/backend/.github/skills/gpu-acceleration
Command: npx skills add https://github.com/dsa110/dsa110-contimg --skill gpu-acceleration-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill provides expert guidance on configuring and utilizing GPU acceleration within the DSA-110 imaging pipeline, significantly speeding up processing times.

Core Features & Use Cases

  • GPU Configuration: Details hardware specs, driver versions, and CUDA compatibility for specific machines (e.g., h17).
  • Usage Methods: Demonstrates how to leverage GPUs via Docker, specific device selection, and environment variables.
  • WSClean Optimization: Explains optimal WSClean gridder settings (IDG modes) for different GPU memory constraints.
  • CuPy Integration: Shows how to use CuPy for GPU-accelerated photometry on large datasets.
  • Use Case: Dramatically reduce the time it takes to generate astronomical images by enabling GPU acceleration for WSClean, allowing for faster scientific discovery.

Quick Start

Run the wsclean command with GPU acceleration enabled using the dsa110-contimg:gpu Docker image.

Frequently Asked Questions about GPU Acceleration Skill

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

FAQPage Schema
How do I enable GPU acceleration for WSClean in Docker?▼

Run WSClean with GPU acceleration by deploying the dsa110-contimg:gpu Docker image and configuring specific device selections alongside environment variables. This configuration provides the CUDA runtime context required for NVIDIA GPU execution.

What are the optimal WSClean gridder settings for limited GPU memory?▼

Optimal WSClean gridder settings for limited GPU memory involve selecting appropriate IDG modes. This configuration balances imaging speed against memory constraints to maximize hardware utilization during the continuum imaging pipeline.

How does CuPy integration accelerate photometry on large datasets?▼

CuPy integration accelerates photometry by offloading array computations directly to the NVIDIA GPU. This approach dramatically speeds up data processing on large astronomical datasets compared to CPU-bound methods.

What hardware and CUDA driver versions are required for DSA-110 GPU imaging?▼

DSA-110 GPU imaging requires specific NVIDIA hardware and compatible CUDA driver versions. The Skill details exact machine specifications like h17 to ensure optimal performance and environment compatibility.

Why is my GPU acceleration not working in the DSA-110 imaging pipeline?▼

GPU acceleration issues in the DSA-110 pipeline often stem from incorrect environment variables, incompatible CUDA drivers, or improper Docker device selection. The Skill provides troubleshooting steps to resolve these configuration conflicts.

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