## Section: New Results

Keywords : numerical simulation, computational steering, code coupling, data redistribution, scientific visualization.

### Computational steering environment for distributed numerical simulations

Participants : Olivier Coulaud, Aurélien Esnard, Nicolas Richart, Mathieu Souchaud.

**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 [6] .

**Placement approach for redistribution.**
We have introduced in [69] 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.