VUDA: Breaking CUDA-Vulkan Isolation for Spatial Sharing of Compute and Graphics on the Same GPU

2026-05-02

AI Summary

This work addresses the inability of CUDA and Vulkan to execute compute and graphics tasks concurrently on GPUs due to scheduling isolation, which severely limits hardware utilization. To overcome this limitation, the authors present the first cross-ecosystem spatial sharing solution between CUDA and Vulkan. Their approach introduces driver-level mechanisms—including channel redirection, virtual address space merging, and page table grafting—to unify scheduling and memory address spaces without requiring data copies. A lightweight developer annotation API is also provided to facilitate integration. Evaluated on representative embodied AI workloads, the system achieves up to 85% higher throughput compared to a time-multiplexing baseline, while significantly reducing end-to-end latency and improving overall GPU utilization.

Abstract

GPU-based simulation environments for embodied AI interleave physics simulation (CUDA) and photorealistic rendering (Vulkan) on a single device. We observe that two foundational scenarios -- simulation data generation and RL training -- can be naturally adapted to execute their simulation and rendering phases concurrently, presenting a significant opportunity to improve GPU utilization through spatial multiplexing. However, a fundamental obstacle we term execution isolation prevents this: CUDA and Vulkan create separate GPU contexts whose channels are bound to different scheduling groups, confining compute and graphics to mutually exclusive time slices. Existing spatial-sharing techniques are limited to the CUDA ecosystem, while temporal-sharing approaches underutilize available resources. This paper presents VUDA, a system that breaks execution isolation to enable spatial parallelism between CUDA compute and Vulkan graphics workloads. VUDA is built on two key observations: although CUDA and Vulkan expose different programming abstractions, their execution paths converge to a common channel primitive at the driver and hardware level; meanwhile, their virtual-address spaces are inherently disjoint, making safe page-table merging feasible without remapping. VUDA exposes a thin API for developers to annotate co-schedulable CUDA streams, and realizes spatial sharing through channel redirection into Vulkan's scheduling domain and page-table grafting to unify address spaces, eliminating all data copying on the critical path. Experiments on representative embodied-AI workloads show that VUDA delivers up to 85% higher throughput than temporal-sharing baselines, while improving GPU utilization and reducing end-to-end latency.

Problem

• execution isolation
• spatial sharing
• CUDA-Vulkan interoperability
• GPU utilization
• concurrent execution

Innovation

• spatial multiplexing
• CUDA-Vulkan co-scheduling
• page-table grafting
• GPU utilization
• execution isolation