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High Performance Computational Fluid Dynamics on Clusters and Clouds: the ADAPT Experience

Abstract : 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 problem solving of CFDs in a classic view corresponding to developments in MPI and in a view that leads us to represent ADAPT 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 system in 2D and 3D using MPI. A two-level parallelization is used in our implementation to take advantage of the current distributed multicore machines. A balanced distribution of the computational load is obtained by using the decomposition of the domain using METIS, as well as a relevant resolution of our very large linear system using the parallel solver MUMPS (Massive Parallel MUltifrontal Solver). Our second contribution illustrates how to imagine the ADAPT framework, as depicted in the first contribution, as a Service. We transform the framework (in fact, a part of the framework) as a DAG (Direct Acyclic Graph) in order to see it as a scientific workflow. Then we introduce new policies inside the RedisDG workflow engine, in order to schedule 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
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Contributor : Imad Kissami <>
Submitted on : Wednesday, January 31, 2018 - 4:09:15 PM
Last modification on : Tuesday, October 20, 2020 - 3:56:17 PM
Long-term archiving on: : Friday, May 25, 2018 - 7:12:56 PM


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  • HAL Id : tel-01697892, version 1


Imad Kissami. High Performance Computational Fluid Dynamics on Clusters and Clouds: the ADAPT Experience. Computer Science [cs]. Université Paris 13, 2017. English. ⟨tel-01697892⟩



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