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Parallel algorithms for tracking of particles

Abstract : The complexity of these new generations of distributed architectures is essencially due to a high number of multi-core nodes. Most of the nodes can be heterogeneous and sometimes remote. Today, nor the high number of nodes, nor the processes that compose the nodes are exploited by most of applications and numerical libraries. The approach of most of parallel libraries (PBLAS, ScalAPACK, P_ARPACK) consists in implementing the distributed version of its base operations, which means that the subroutines of these libraries can not adapt their behaviors to the data types. These subroutines must be defined once for use in the sequential case and again for the parallel case. The object-oriented approach allows the modularity and scalability of some digital libraries (such as PETSc) and the reusability of sequential and parallel code. This modern approach to modelize sequential/parallel libraries is very promising because of its reusability and low maintenance cost. In industrial applications, the need for the use of software engineering techniques for scientific computation, whose reusability is one of the most important elements, is increasingly highlighted. However, these techniques are not yet well defined. The search for methodologies for designing and producing reusable libraries is motivated by the needs of the industries in this field. The main objective of this thesis is to define strategies for designing a parallel library for Lagrangian particle tracking using a component approach. These strategies should allow the reuse of the sequential code in the parallel versions while allowing the optimization of the performances. The study should be based on a separation between the control flow and the data flow management. It should extend to models of parallelism allowing the exploitation of a large number of cores in shared and distributed memory.
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Submitted on : Sunday, April 7, 2019 - 3:23:03 AM
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  • HAL Id : tel-02091283, version 2

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Florent Bonnier. Parallel algorithms for tracking of particles. Distributed, Parallel, and Cluster Computing [cs.DC]. Université Paris-Saclay, 2018. English. ⟨NNT : 2018SACLV080⟩. ⟨tel-02091283v2⟩

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