Abstract
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.
Highlights
Since the emergence of mobile devices such as smartphones and tablets, the computing environment has been rapidly shifting from personal computers (PCs) to mobile platforms
Combined with the kd-tree encoding scheme that was proposed by Seo et al [7] (Section 3.2), we show that both the triangular mesh and the essential kd-tree structure of such a large Power Plant scene with more than 12 million triangles can be successfully loaded onto the graphics processing unit (GPU) memory of a low-end mobile device for efficient distributed ray tracing
The ray tracer was optimized on the mobile GPU with the OpenCL 2.0 API, in which work-groups of 8 × 8 work-items were applied for measuring timings
Summary
Since the emergence of mobile devices such as smartphones and tablets, the computing environment has been rapidly shifting from personal computers (PCs) to mobile platforms. In contrast to the rasterization-based renderers that adopt only approximate (often inaccurate) rendering models, ray tracing is capable of creating various advanced rendering effects, such as shadow, reflection, refraction and diffuse interreflection, in a physically correct manner Note that such ray tracing effects could enhance immersive experiences markedly for augmented reality (AR) or mixed reality (MR) users who utilize mobile extended reality (XR). This work was concerned with exploring the possibility of mobile cluster-based distributed computing as an effective mechanism for ray tracing large 3D scenes that consist of a few million to 12 million triangles. This paper presents a mobile distributed GPU ray tracing scheme that is well suited for rendering large 3D scenes with many millions of triangles, aiming at interactive speeds on a small-scale mobile cluster. The paper is concluded in the final section (Section 6)
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