Un modèle de programmation à grain fin pour la parallélisation de solveurs linéaires creux

Abstract : Solving large sparse linear system is an essential part of numerical simulations. These resolve can takeup to 80% of the total of the simulation time.An efficient parallelization of sparse linear kernels leads to better performances. In distributed memory,parallelization of these kernels is often done by changing the numerical scheme. Contrariwise, in sharedmemory, a more efficient parallelism can be used. It’s necessary to use two levels of parallelism, a first onebetween nodes of a cluster and a second inside a node.When using iterative methods in shared memory, task-based programming enables the possibility tonaturally describe the parallelism by using as granularity one line of the matrix for one task. Unfortunately,this granularity is too fine and doesn’t allow to obtain good performance.In this thesis, we study the granularity problem of the task-based parallelization. We offer to increasegrain size of computational tasks by creating aggregates of tasks which will become tasks themself. Thenew coarser task graph is composed by the set of these aggregates and the new dependencies betweenaggregates. Then a task scheduler schedules this new graph to obtain better performance. We use as examplethe Incomplete LU factorization of a sparse matrix and we show some improvements made by this method.Then, we focus on NUMA architecture computer. When we use a memory bandwidth limited algorithm onthis architecture, it is interesting to reduce NUMA effects. We show how to take into account these effects ina task-based runtime in order to improve performance of a parallel program.
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Submitted on : Thursday, November 19, 2015 - 10:56:21 AM
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Corentin Rossignon. Un modèle de programmation à grain fin pour la parallélisation de solveurs linéaires creux. Calcul parallèle, distribué et partagé [cs.DC]. Université de Bordeaux, 2015. Français. ⟨NNT : 2015BORD0094⟩. ⟨tel-01230876⟩



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