cuda-interop

Map ovrtx render output to CUDA memory or CUDA arrays with timeline semaphores.

201|25|Updated Dec 18, 2025
One-click install
npx skills add https://github.com/NVIDIA-Omniverse/ovrtx --skill cuda-interop
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cuda-interop
Source: https://github.com/NVIDIA-Omniverse/ovrtx/tree/main/.agents/skills/cuda-interop
Command: npx skills add https://github.com/NVIDIA-Omniverse/ovrtx --skill cuda-interop

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enable GPU-side interop by mapping ovrtx render output to CUDA memory or CUDA arrays for zero-copy or efficient access, and ensure correct synchronization with CUDA consumers.

Core Features & Use Cases

  • CUDA interop patterns for rendering pipelines involving CUDA arrays, timeline semaphores, and Vulkan shared memory.
  • Guidance on mapping render outputs to CUDA memory or CUDA arrays, managing synchronization, and coordinating with Vulkan occupancy/shared memory patterns.
  • Use Case: Integrate ovrtx-rendered output into a CUDA-based post-processing or visualization pipeline.

Quick Start

Configure your pipeline to map ovrtx render output to CUDA memory and coordinate synchronization with your CUDA consumer.

Frequently Asked Questions about cuda-interop

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

FAQPage Schema
How do I map render output to CUDA memory for zero-copy access?▼

Map render output to CUDA memory by configuring your pipeline to map outputs directly to CUDA memory or CUDA arrays, enabling zero-copy access for efficient CUDA consumers.

What are timeline semaphores used for in CUDA and Vulkan interop?▼

Timeline semaphores in CUDA and Vulkan interop enforce correct device mapping and synchronization semantics, applying explicit wait and signal patterns for coordinated shared memory access.

Can I integrate CUDA compute with a Vulkan rendering pipeline?▼

Yes, you can integrate CUDA compute with a Vulkan rendering pipeline by coordinating synchronization through shared memory and timeline semaphores across Python and C/C++ implementations.

How do I synchronize CUDA consumers with external rendering pipelines?▼

Synchronize CUDA consumers with external rendering pipelines by applying explicit wait and signal patterns using timeline semaphores, ensuring correct synchronization semantics and shared memory access.

Do I need specific synchronization patterns for USD-based rendering paths using CUDA?▼

Yes, USD-based rendering paths integrating CUDA require explicit wait and signal patterns using timeline semaphores to enforce correct device mapping and synchronized access across Python and C/C++ implementations.