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Adaptive and generic parallel exact linear algebra

Abstract : Triangular matrix decompositions are fundamental building blocks in computational linear algebra. They are used to solve linear systems, compute the rank, the determinant, the null-space or the row and column rank profiles of a matrix. The project of my PhD thesis is to develop high performance shared memory parallel implementations of exact Gaussian elimination.In order to abstract the computational code from the parallel programming environment, we developed a domain specific language, PALADIn: Parallel Algebraic Linear Algebra Dedicated Interface, that is based on C/C + + macros. This domain specific language allows the user to write C + + code and benefit from sequential and parallel executions on shared memory architectures using the standard OpenMP, TBB and Kaapi parallel runtime systems and thus providing data and task parallelism.Several aspects of parallel exact linear algebra were studied. We incrementally build efficient parallel kernels, for matrix multiplication, triangular system solving, on top of which several variants of PLUQ decomposition algorithm are built. We study the parallelization of these kernels using several algorithmic variants: either iterative or recursive and using different splitting strategies.We propose a recursive Gaussian elimination that can compute simultaneously therow and column rank profiles of a matrix as well as those of all of its leading submatrices, in the same time as state of the art Gaussian elimination algorithms. We also study the conditions making a Gaussian elimination algorithm reveal this information by defining a new matrix invariant, the rank profile matrix.
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Submitted on : Tuesday, January 9, 2018 - 5:58:07 PM
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Ziad Sultan. Adaptive and generic parallel exact linear algebra. General Mathematics [math.GM]. Université Grenoble Alpes, 2016. English. ⟨NNT : 2016GREAM030⟩. ⟨tel-01679285⟩



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