A Memory Efficient Parallel Particle-Based Volume Rendering for Large-Scale Distributed Unstructured Volume Datasets in HPC Environments

In recent years, the size and complexity of the datasets generated by the large-scale numerical simulations using modern HPC (High Performance Computing) systems have continuously increasing. These generated datasets can possess different formats, types, and attributes. In this work, we have focused on the large-scale distributed unstructured volume datasets, which are still applied on numerical simulations in a variety of scientific and engineering fields. Although volume rendering is one of the most popular techniques for analyzing and exploring a given volume data, in the case of unstructured volume data, the time-consuming visibility sorting becomes problematic as the data size increases. Focusing on an effective volume rendering of large-scale distributed unstructured volume datasets generated in HPC environments, we opted for using the well-known PBVR (Particle-based Volume Rendering) method. Although PBVR does not require any visibility sorting during the rendering process, the CPU-based approach has a notorious image quality and memory consumption tradeoff. This is because that the entire set of the intermediate rendering primitives (particles) was required to be stored a priori to the rendering processing. In order to minimize the high pressure on the memory consumption, we propose a fully parallel PBVR approach, which eliminates the necessity for storing these intermediate rendering primitives, as required by the existing approaches. In the proposed method, each set of the rendering primitives is directly converted to a partial image by the processes, and then they are gathered and merged by the utilized parallel image composition library (234Compositor). We evaluated the memory cost and processing time by using a real CFD simulation result, and we could verify the effectiveness of our proposed method compared to the already existing parallel PBVR method.