Department of Computer Science and Engineering, Sogang University, Seoul 04107, Korea.
Department of Multimedia, Dongguk University, Seoul 04620, Korea.
Sensors (Basel). 2022 Jan 10;22(2):491. doi: 10.3390/s22020491.
Cluster computing has attracted much attention as an effective way of solving large-scale problems. However, only a few attempts have been made to explore mobile computing clusters that can be easily built using commodity smartphones and tablets. To investigate the possibility of mobile cluster-based rendering of large datasets, we developed a mobile GPU ray tracer that renders nontrivial 3D scenes with many millions of triangles at an interactive frame rate on a small-scale mobile cluster. To cope with the limited processing power and memory space, we first present an effective 3D scene representation scheme suitable for mobile GPU rendering. Then, to avoid performance impairment caused by the high latency and low bandwidth of mobile networks, we propose using a static load balancing strategy, which we found to be more appropriate for the vulnerable mobile clustering environment than a dynamic strategy. Our mobile distributed rendering system achieved a few frames per second when ray tracing 1024 × 1024 images, using only 16 low-end smartphones, for large 3D scenes, some with more than 10 million triangles. Through a conceptual demonstration, we also show that the presented rendering scheme can be effectively explored for augmenting real scene images, captured or perceived by augmented and mixed reality devices, with high quality ray-traced images.
集群计算作为一种解决大规模问题的有效方法已经引起了广泛关注。然而，只有少数尝试探索使用商用智能手机和平板电脑轻松构建的移动计算集群。为了研究基于移动集群进行大数据集渲染的可能性，我们开发了一种移动 GPU 光线追踪器，该光线追踪器可以在小规模移动集群上以交互帧率渲染具有数百万个三角形的复杂 3D 场景。为了应对有限的处理能力和内存空间，我们首先提出了一种适用于移动 GPU 渲染的有效 3D 场景表示方案。然后，为了避免移动网络的高延迟和低带宽带来的性能影响，我们提出使用静态负载平衡策略，与动态策略相比，我们发现这种策略更适合脆弱的移动集群环境。当使用仅 16 部低端智能手机对具有超过 1000 万个三角形的大型 3D 场景进行 1024×1024 图像的光线追踪时，我们的移动分布式渲染系统每秒可以实现几帧，通过概念验证，我们还展示了所提出的渲染方案可以有效地探索用于增强增强现实和混合现实设备捕获或感知的真实场景图像，使其具有高质量的光线追踪图像。
