Classical direct volume rendering techniques accumulate color and opacity contributions using the standard volume rendering equation approximated by alpha blending. However, such standard rendering techniques, often also aiming at visual realism, are not always adequate for efficient data exploration, especially when large opaque areas are present in a data set, since such areas can occlude important features and make them invisible. On the other hand, the use of highly transparent transfer functions allows viewing all the features at once, but often makes these features barely visible. In order to enhance feature visibility, we present in this paper a straightforward rendering technique that consists of modifying the traditional volume rendering equation. Our approach does not require an opacity transfer function, and instead is based on a function quantifying the relative importance of each voxel in the final rendering called relevance function. This function is subsequently used to dynamically adjust the opacity of the contributions per pixel. We conduct experiments with a number of possible relevance functions in order to show the influence of this parameter. As will be shown by our comparative study, our rendering method is much more suitable than standard volume rendering for interactive data exploration at a low extra cost. Thereby, our method avoids feature visibility restrictions without relying on a transfer function and yet maintains a visual similarity with standard volume rendering.

In the field of Volume Rendering, the pre-integration of arbitrary transfer functions has certainly led to the most significant and convincing results both quality and performance wise, allowing high quality visualization on standard PC consumer graphics. By showing that the ideal scalar signal along the cast rays is better approximated by a succession of polynomial curves as opposed to linear segments, we propose a new method for pre-integrated volume rendering. This method is based on a second order polynomial interpolation of the scalar values, allowing it to converge more rapidly towards the integration of a volume reconstructed by a trilinear filter. This approach manages to capture the smoothness of the volume's details without the need of further ray resampling, and consequently succeeds in reducing the visual artefacts in comparison to previous techniques. Futhermore, we adapt an existing technique to compute our pre-integration tables using the GPU, thus making our approach suitable for transfer function manipulations.

Over the last couple of years, several dedicated grid platforms have been set up to test applications and middleware for grids. Among these is Grid'5000, a reconfigurable platform gathering resources at nine remote geographical sites in France. This paper presents one of the eight experiments that have tested software scalability at the scale of a thousand processors (i.e. 500-1000) on this grid testbed. The experiment aims at analyzing the behavior of a geophysical application (a seismic ray tracing in a 3D mesh of the Earth). The application is computationally intensive but requires an all-to-all communication phase during which processors exchange their results, which has shown to be a real bottleneck on many hardware platforms. We analyze various runs and show that this application scales well up to about 500 processors on such a grid.

Seismic tomography enables to model the internal structure of the Earth. In order to improve the precision of existing models, a huge amount of acquired seismic data must be analyzed. The analysis of such massive data requires a considerable computing power, which can only be delivered by parallel computational equipments. Yet, parallel computation is not sufficient for the task: we also need algorithms to automatically concentrate the computations on the most relevant data parts. The objective of the paper is to present such an algorithm. From an initial regular mesh in which cells carry data with varying relevance, we present a method to aggregate elementary cells so as to homogenize the relevance of data. The result is an irregular mesh, which has the advantage over the initial mesh of having orders of magnitude less cells while preserving the geophysical meaning of data. We present both a sequential and a parallel algorithm to solve this problem under the hypotheses and constraints inherited from the geophysical context.

In order to get a parallel solution from a system of affine recurrence equations, a space-time transformation must first be determined. Such a transformation is characterized by a schedule and an allocation. In the context of data parallelism, efficient compilers require among other criteria appropriate data compiling techniques. These techniques should take into account the communication primitives of the target architecture (in particular broadcast primitives if any) and minimize the number of communications. The objective of this paper is to deal with these two questions (efficient use of broadcast capabilities and communication minimization) in order to determine efficient space-time transformations from systems of affine recurrence equations.>

The authors present a method for the mapping of systolic algorithms that use the minimal number of processors. This method is based on geometrical interpretations on convex polyhedra in Z/sup n/. The authors present a recurrence equation model defining the target problems for systolic program derivation. Some geometrical tools on convex polyhedra in Z/sup n/ are given. They are first used to model systolic timing allocation in terms of geometrical structures, and then to deduce a processor array mapping method that automatically gives space-optimal mappings. The results are used to derive two space-optimal mappings of the Gaussian elimination algorithm.>