Accelerating path tracing rendering with Multi-GPU in Blender cycles
Jiuyi Chen, Ling Chen, Zhibin Yu · 2023
Metaverse is one of the current hottest research fields, which not only requires rendering techniques such as path tracing but also rendering engines such as Blender. However, rendering scenes by path tracing is a complex and computationally intensive process, especially for large scenes, causing catastrophically slow rendering using only a single GPU.To improve rendering performance, we use multiple GPUs to achieve parallel rendering in Blender Cycles for path tracing. First, we distribute the workload based on pixel blocks. Each GPU renders one of the image fragments to finally combine the rendering results into a complete image. Second, we propose a Dynamic Workload Adjustment Algorithm (DWAA) to solve the workload imbalance problem. DWAA takes advantage of the fact that path tracing requires multiple samples, so it increases or decreases the work area of each GPU for the next ray sample according to the rendering time of each GPU’s last path-tracing sample, which can reduce GPU idle time and improve overall rendering performance.To evaluate the Multi-GPUs rendering approach and DWAA, we use two different scale scenes - Babershop and Junkshop, and test them with different GPU clusters consisting of 2, 3, and 4 NVIDIA A100 GPUs respectively. The results show that compared to a single GPU, 2 GPUs can accelerate up to 1.98x, 3 GPUs up to 2.94x, and 4 GPUs up to 3.9x, which has high scalability and the parallelism is close to linear increase.