Wenxin Zheng

Bin Xu, Pengfei Hu, Wenxin Zheng et al.

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.

Kaikai Deng, Dong Zhao, Wenxin Zheng et al.

Millimeter wave radar is gaining traction recently as a promising modality for enabling pervasive and privacy-preserving gesture recognition. However, the lack of rich and fine-grained radar datasets hinders progress in developing generalized deep learning models for gesture recognition across various user postures (e.g., standing, sitting), positions, and scenes. To remedy this, we resort to designing a software pipeline that exploits wealthy 2D videos to generate realistic radar data, but it needs to address the challenge of simulating diversified and fine-grained reflection properties of user gestures. To this end, we design G3R with three key components: (i) a gesture reflection point generator expands the arm's skeleton points to form human reflection points; (ii) a signal simulation model simulates the multipath reflection and attenuation of radar signals to output the human intensity map; (iii) an encoder-decoder model combines a sampling module and a fitting module to address the differences in number and distribution of points between generated and real-world radar data for generating realistic radar data. We implement and evaluate G3R using 2D videos from public data sources and self-collected real-world radar data, demonstrating its superiority over other state-of-the-art approaches for gesture recognition.

Zhenyuan Yang, Wenxin Zheng, Mingyu Li

GPU sharing is critical for maximizing hardware utilization in modern data centers. However, existing approaches present a stark trade-off: coarse-grained temporal multiplexing incurs severe tail-latency spikes for interactive services, while fine-grained spatial partitioning often necessitates invasive kernel modifications that compromise behavioral equivalence. We present DetShare, a novel GPU sharing system that prioritizes determinism and transparency. DetShare ensures semantic determinism (unmodified kernels yield identical results) and performance determinism (predictable tail latency), all while maintaining complete transparency (zero code modification). DetShare introduces GPU coroutines, a new abstraction that decouples logical execution contexts from physical GPU resources. This decoupling enables flexible, fine-grained resource allocation via lightweight context migration. Our evaluation demonstrates that DetShare improves training throughput by up to 79.2% compared to temporal sharing. In co-location scenarios, it outperforms state-of-the-art baselines, reducing P99 tail latency by 15.1% without compromising throughput. Furthermore, through workload-aware placement and our TPOT-First scheduling policy, DetShare decreases average inference latency by 69.1% and reduces Time-Per-Output-Token (TPOT) SLO violations by 21.2% relative to default policies.