Distributed massive model rendering

Abstract

Graphics models are getting increasingly bulkier with detailed geometry, textures, normal maps, etc. There is a lot of interest to model and navigate through detailed models of large monuments. Many monuments of interest have both rich detail and large spatial extent. Rendering them for navigation on a single workstation is practically impossible, even given the power of today's CPUs and GPUs. Many models may not fit the GPU memory, the CPU memory, or even the secondary storage of the CPU. Distributed rendering using a cluster of workstations is the only way to navigate through such models. In this paper, we present a design of a distributed rendering system intended for massive models. Our design has a server that holds the skeleton of the whole model, namely, its scenegraph with actual geometry replaced by bounding boxes at all levels. The server divides the screen space among a number of clients and sends them a list of objects they need to render using a frustum culling step. The clients use 2 GPUs with one devoted to visibility culling and the other to rendering. Frustum culling at the server, visibility culling on one GPU, and rendering on the second GPU form the stages of our distributed rendering pipeline. We describe the design and implementation of our system and demonstrate the results of rendering relatively large models using different clusters of clients in this paper.

High-performance computer graphics technologies in engineering applications

“…A heterogeneous graphics system is a single or a cluster of workstations which integrate multi-core central processing units (CPUs) and multiple graphics processing units (GPUs). Such systems have been used in in situ visualization (Bauer et al , 2016), massive model rendering (Revanth and Narayanan, 2012) and multi-user virtual environments (Engelbrecht and Gilmore, 2017). In the system, the data set can be stored in bulk memory space like disks.…”