COTS Cluster-based Sort-last Rendering: Performance Evaluation and Pipelined Implementation

Sort-last parallel rendering is an efficient technique to visualize huge datasets on COTS clusters. The dataset is subdivided and distributed across the cluster nodes. For every frame, each node renders a full resolution image of its data using its local GPU, and the images are composited together using a parallel image compositing algorithm. In this paper, we present a performance evaluation of standard sort-last parallel rendering methods and of the different improvements proposed in the literature. This evaluation is based on a detailed analysis of the different hardware and software components. We present a new implementation of sort-last rendering that fully overlaps CPU(s), GPU and network usage all along the algorithm. We present experiments on a 3 years old 32-node PC cluster and on a 1.5 years old 5-node PC cluster, both with Gigabit interconnect, showing volume rendering at respectively 13 and 31 frames per second and polygon rendering at respectively 8 and 17 frames per second on a 1024×768 render area, and we show that our implementation outperforms or equals many other implementations and specialized visualization clusters

Doctor of Philosophy in Computing

dissertationThe aim of direct volume rendering is to facilitate exploration and understanding of three-dimensional scalar fields referred to as volume datasets. Improving understanding is done by improving depth perception, whereas facilitating exploration is done by speeding up volume rendering. In this dissertation, improving both depth perception and rendering speed is considered. The impact of depth of field (DoF) on depth perception in direct volume rendering is evaluated by conducting a user study in which the test subjects had to choose which of two features, located at different depths, appeared to be in front in a volume-rendered image. Whereas DoF was expected to improve perception in all cases, the user study revealed that if used on the back feature, DoF reduced depth perception, whereas it produced a marked improvement when used on the front feature. We then worked on improving the speed of volume rendering on distributed memory machines. Distributed volume rendering has three stages: loading, rendering, and compositing. In this dissertation, the focus is on image compositing, more specifically, trying to optimize communication in image compositing algorithms. For that, we have developed the Task Overlapped Direct Send Tree image compositing algorithm, which works on both CPU- and GPU-accelerated supercomputers, which focuses on communication avoidance and overlapping communication with computation; the Dynamically Scheduled Region-Based image compositing algorithm that uses spatial and temporal awareness to efficiently schedule communication among compositing nodes, and a rendering and compositing pipeline that allows both image compositing and rendering to be done on GPUs of GPU-accelerated supercomputers. We tested these on CPU- and GPU-accelerated supercomputers and explain how these improvements allow us to obtain better performance than image compositing algorithms that focus on load-balancing and algorithms that have no spatial and temporal awareness of the rendering and compositing stages