gVulkan: Scalable GPU Pooling for Pixel-Grained Rendering in Ray Tracing

Authors: 

Yicheng Gu, Yun Wang, Yunfan Sun, Yuxin Xiang, Yufan Jiang, Xuyan Hu, Zhengwei Qi, and Haibing Guan, Shanghai Jiao Tong University

Abstract: 

Ray tracing rendering technology enhances scene realism and offers immersive experiences. However, it demands significant computational resources to trace and compute light-object interactions. As a result, traditional local GPU rendering might not meet the demands for high image quality and low latency. Moreover, many applications are tailored to utilize the resources of a single GPU, limiting their capacity to increase computational power through additional GPUs.

This paper presents gVulkan, the first transparent multi-GPU acceleration rendering solution for Vulkan-based ray tracing. To address the bottleneck caused by limited local GPU resources, gVulkan can offload ray tracing rendering to the cloud via API-forwarding. In the cloud, gVulkan employs Split Frame Rendering (SFR) to enable an arbitrary number of GPUs to accelerate rendering in parallel, while dynamically self-rebalancing the workload at a pixel-grained level across GPUs. Experiments demonstrate that gVulkan can accelerate Vulkan-based ray tracing programs in an application-unaware manner. By dynamically rebalancing each GPU's workload, gVulkan achieves good linearity with 3.81× speedup across 4 GPUs on average.