Section: New Results
Computational steering environment for distributed numerical simulations
Steering of a legacy simulation in astrophysics. This year, we have experimentally validated the EPSN framework with a legacy simulation in astrophysics, called Gadget2. It has demonstrated the interest of parallel visualization and rendering techniques to reduce the steering overhead and achieve better performance than for classical steering environment. In our case study, Gadget2 simulates the birth of a galaxy, which is represented by a gas cloud that collapses gravitationally until a central shock. The gas cloud is modelled by 1,000,000 particles distributed on 60 processes. The simulation has been deployed on the Grid'5000 cluster and connected to a visualization cluster with a 2×2 tiled-display. This experiment also validates the ability of EPSN to understand dynamic and complex data distribution. These results has been published in  .
Placement approach for redistribution. We have introduced in  a new redistribution approach, called placement , that is well adapted to the context of M×N computational steering. In this case, the data distribution on the visualization code is not initially defined. This offers the opportunity for the redistribution layer to choose it at run-time in "the best way". Our strategy consists in the placement of the data elements, initially distributed on M simulation processes, to the N visualization processes. In order to equilibrate the number of elements on the visualization code and to minimize the number of messages, one simply realizes the intersection of the two distribution patterns, that results in the generation of M + N-GCD(M, N) messages. In irregular cases, this requires to split the elements handled by a simulation process into several messages. For particle data, the split operator is quite trivial, while for unstructured meshes, it is defined thanks to graph partitioning techniques as those provided by Scotch or METIS . The RedGRID library has been extended to support this new redistribution approach, that we already use in EPSN .
In the context of the ANR MASSIM, we are now considering more complex data structures such as hierarchical grids with large amount of data.
Model for the steering of parallel-distributed simulations. The model that we have proposed in the EPSN framework can only steer efficiently SPMD simulations. A natural development is to consider more complex simulations such as coupled SPMD codes called M-SPMD (Multiple SPMD like multi-scale simulation for ``crack-propagation'') and client/server simulation codes. In order to steer these kinds of simulation, we have designed an extension to the Hierarchical Task Model (HTM), that affords to solve the coherency problem for such complex applications. In future works, we will implement our model in the EPSN framework and validate it with the multi-scale simulation for ``crack-propagation''developed in the project.
Dynamic adaptation. We intend through the ARC COA to find a common approach for the dynamic adaptation and the computational steering. In this way, we have proposed a component model that afford to steering and adapt dynamically simulations. To validate this model we have integrated the EPSN environment into the dynamic adaptation framework called Dynaco, developed in the Paris project. It validates the idea that the steering and the dynamic adaptation are based on the same concepts.