SUMMARY This paper describes a new fast and scalable parallel algorithm to automatically determine catchment basin of rivers in large digital elevation models (DEMs). This algorithm is based on the construction of a minimal spanning tree, via a hierarchy of graphs, modeling the water route on the DEM. It combines different techniques used in hydrogeology, image processing and graph theory to obtain the most accurate results in terms of geomorphology without any preprocessing. The method tends to exploit the most of the DEMs, avoiding misleading inconsistencies DEMs contain. It has been designed to be entirely parallel and scalable for architectures such as PC clusters. Some experiments are presented to show accuracy, efficiency and scalability on huge DEMs. Copyright © 2012 John Wiley & Sons, Ltd.

Networked virtual environments like Second Life enable distant people to meet for leisure as well as work. But users are represented through avatars controlled by keyboards and mouses, leading to a low sense of presence especially regarding body language. Multi-camera real-time 3D modeling offers a way to ensure a significantly higher sense of presence. But producing quality geometries, well textured, and to enable distant user tele-presence in non trivial virtual environments is still a challenge today.

We propose to illustrate a generic Levels Of Details (LOD) technique which is a key point to construct distributed VR application involving large static or dynamic datasets, issued from measurements or big simulations. This demo focus on a parallel LOD algorithm for GIS and presents a 3D textured section of the Loire river near Orléans. Since all visualized data are handle in RAM memory without complex pre-processing, this method is transposable into more dynamic context where texture data are generated from simulations.

We describe a parallel framework for interactive smooth rendering of massive terrains. We define a parallelization scheme for level of detail algorithms in cluster-based environments. The scheme relies on modern PC clusters capabilities to address the scalability issue of level of detail algorithms. To achieve this, we propose an eficient tile-based data partitioning method that allows both reducing load imbalance and solving the well-known border problem. At runtime level of detail computations are performed in parallel on cluster nodes. A hierarchical view frustum culling combined to a compression mechanism harnessing the frame-to-frame coherence are used to drastically reduce the inter-tasks communication overhead. We take into account level of detail algorithms visual quality issue by providing geomorphing and texturing supports. We are able to interactively and smoothly render terrains composed of hundreds of millions to billions of polygons on a cluster of 8 PCs.

Net Juggler is an open source library that turns a commodity component cluster running the VR Juggler platform on each node into a single VR Juggler image cluster. Application parallelization is transparent to the user and leads to high performance executions even with limited bandwidth networks.

Parallelism suffers from a lack of programming languages both simple to handle and able to take advantage of the power of present parallel computers. If parallelism expression is too high level, compilers have to perform complex optimizations leading often to poor performances. One the other hand, too low level parallelism transfers difficulties toward the programmer. We propose a programming language that integrates both a synchronous data parallel programming model and an asynchronous execution model. The synchronous data parallel programming model allows safe program design. The asynchronous execution model yields an efficient execution on present MIMD architectures without any program transformation. Our language relies an logical instruction ordering exploited by specific send/receive communications. It allows one to express only the effective data dependences between processors. This ability is enforced by a possible send/receive unmatching, useful for irregular algorithms. A sparse vector computation exemplifies our language potentialities.