Graph partitioning is used to solve the problem of distributing computations to a number of processors, in order to improve the performance of time consuming applications in parallel environments. A common approach to solve this problem is based on a multilevel framework, where the graph is firstly coarsened to a smaller instance and then it is partitioned in a number of parts using recursive bisection (RB) based methods. However, in applications where initial fixed vertices are used to model additional constraints of the problem, RB based methods often fail to produce partitions of good quality. In this paper, we propose a new direct k-way greedy graph growing algorithm, called KGGGP, that overcomes this issue and succeeds to produce partition with better quality than RB while respecting the constraint of fixed vertices. In the experimental section, we present results which compare KGGGP against state-of-theart methods for graphs available from the popular DIMACS'10 collection.

In the context of scientific computing, the computational steering consists in the coupling of numerical simulations with 3D visualization systems through the network. This allows scientists to monitor online the intermediate results of their computations in a more interactive way than the batch mode, and allows them to modify the simulation parameters on-the-fly. While most of existing computational steering environments support parallel simulations, they are often limited to sequential visualization systems. This may lead to an important bottleneck and increased rendering time. To achieve the required performance for online visualization, we have designed the EPSN framework, a computational steering environment that enables to interconnect legacy parallel simulations with parallel visualization systems. For this, we have introduced a redistribution algorithm for unstructured data, that is well adapted to the context of M × N computational steering. Then, we focus on the design of our parallel viewer and present some experimental results obtained with a particlebased simulation in astrophysics.

Abstract. The on-line visualization and the computational steering of parallel simulations come up against a serious coherence problem. Indeed, data distributed over parallel processes must be accessed carefully to ensure they are presented to the visualization system in a meaningful way. In this paper, we present a solution to the coherence problem for structured parallel simulations. We introduce a hierarchical task model that allows to better grasp the complexity of simulations, too often considered as "single-loop" applications. Thanks to this representation, we can schedule in parallel the request treatments on the simulation processes and satisfy the temporal coherence.