Parallel volume rendering offers a feasible solution to the large data visualization problem by distributing both the data and rendering calculations among multiple computers connected by a network. In sort-last parallel volume rendering, each processor generates an image of its assigned subvolume, which is blended together with other images to derive the final image. Improving the efficiency of this compositing step, which requires interprocesssor communication, is the key to scalable, interactive rendering. The recent trend of using hardware-accelerated volume rendering demands further acceleration of the image compositing step. This paper presents a new optimized parallel image compositing algorithm and its performance on a PC cluster. Our test results show that this new algorithm offers significant savings over previous algorithms in both communication and compositing costs. On a 64-node PC cluster with a 100BaseT network interconnect, we can achieve interactive rendering rates for images at resolutions up to 1024x1024 pixels at several frames per second.

The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.