Concurrent pipeline rendering scheme based on GPU multi-queue and partitioning images

Zixia Qiu; Junchao Ma; Haitang Zhang; Mustafa A. Al Sibahee; Zaid Ameen Abduljabbar; Vincent Omollo Nyangaresi

doi:10.1117/12.2678656

14 April 2023 Concurrent pipeline rendering scheme based on GPU multi-queue and partitioning images

Zixia Qiu, Junchao Ma, Haitang Zhang, Mustafa A. Al Sibahee, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 126340M (2023) https://doi.org/10.1117/12.2678656
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of Graphics Processing Unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of Frames Per Second (FPS) is increased by 1.5 times after using MQCPR.

Citation Download Citation

Zixia Qiu, Junchao Ma, Haitang Zhang, Mustafa A. Al Sibahee, Zaid Ameen Abduljabbar, and Vincent Omollo Nyangaresi "Concurrent pipeline rendering scheme based on GPU multi-queue and partitioning images", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 126340M (14 April 2023); https://doi.org/10.1117/12.2678656

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