We present a cluster-based volume rendering system for roaming very large volumes. This system allows to move a gigabyte-sized probe inside a total volume of several tens or hundreds of gigabytes in real-time. While the size of the probe is limited by the total amount of texture memory on the cluster, the size of the total data set has no theoretical limit. The cluster is used as a distributed graphics processing unit that both aggregates graphics power and graphics memory. A hardware-accelerated volume renderer runs in parallel on the cluster nodes and the final image compositing is implemented using a pipelined sort-last rendering algorithm. Meanwhile, volume bricking and volume paging allow efficient data caching. On each rendering node, a distributed hierarchical cache system implements a global software-based distributed shared memory on the cluster. In case of a cache miss, this system first checks page residency on the other cluster nodes instead of directly accessing local disks. Using two Gigabit Ethernet network interfaces per node, we accelerate data fetching by a factor of 4 compared to directly accessing local disks. The system also implements asynchronous disk access and texture loading, which makes it possible to overlap data loading, volume slicing and rendering for optimal volume roaming.

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 x 768 render area, and we show that our implementation outperforms or equals many other implementations and specialized visualization clusters.

We present the circular incident edge lists (CIEL), a new data structure and a high-performance algorithm for generating a series of iso-surfaces in a highly unstructured grid. Slicing-based volume rendering is also considered. The CIEL data structure represents all the combinatorial information of the grid, making it possible to optimize the classical propagation from local minima paradigm. The usual geometric structures are replaced by a more efficient combinatorial structure. An active edges list is maintained, and iteratively propagated from an iso-surface to the next one in a very efficient way. The intersected cells incident to each active edge are retrieved, and the intersection polygons are generated by circulating around their facets. This latter feature enables arbitrary irregular cells to be treated, such as those encountered in certain computational fluid dynamics (CFD) simulations. Since the CIEL data structure solely depends on the connections between the cells, it is possible to take into account dynamic changes in the geometry of the mesh and in property values, which only requires the sorted extrema list to be updated. Experiments have shown that our approach is significantly faster than classical methods. The major drawback of our method is its memory consumption, higher than most classical methods. However, experimental results show that it stays within a practical range.

Having a better way to represent and to interact with large geological models are topics of high interest in geoscience, and especially for oil and gas companies. We present the design and implementation of a visualization program that involves two main features. It is based on the central data model, in order to display in real time the modifications caused by the modeler. Furthermore, it benefits from the different immersive environments which give the user a much more accurate insight of the model than a regular computer screen. Then, we focus on the difficulties that come in the way of performance.