Haute performance pour le calcul de la dynamique des fluides dans les clusters et les clouds : l'expérience ADAPT
Imad Kissami · HAL (Le Centre pour la Communication Scientifique Directe) · 2017
In this thesis, we present our research work in the field of high performance computing in fluid mechanics (CFD)for cluster and cloud architectures. In general, we propose to develop an efficient solver, called ADAPT, for problemsolving of CFDs in a classic view corresponding to developments in MPI and in a view that leads us to representADAPT as a graph of tasks intended to be ordered on a cloud computing platform.As a first contribution, we propose a parallelization of the diffusion-convection equation coupled to a linear systemin 2D and 3D using MPI. A two-level parallelization is used in our implementation to take advantage of the currentdistributed multicore machines. A balanced distribution of the computational load is obtained by using the decompositionof the domain using METIS, as well as a relevant resolution of our very large linear system using the parallelsolver MUMPS (Massive Parallel MUltifrontal Solver).Our second contribution illustrates how to imagine the ADAPT framework, as depicted in the first contribution, asa Service. We transform the framework (in fact, a part of the framework) as a DAG (Direct Acyclic Graph) in orderto see it as a scientific workflow. Then we introduce new policies inside the RedisDG workflow engine, in order toschedule tasks of the DAG, opportunistically. We introduce into RedisDG the possibility to work with dynamic workers(they can leave or enter into the computing system as they want) and a multi-criteria approach to decide on the“best” worker to choose to execute a task. Experiments are conducted on