AGULLO, Emmanuel; L'EXCELLENT, Jean-Yves; GUERMOUCHE, Abdou

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AGULLO, Emmanuel
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]

L'EXCELLENT, Jean-Yves
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]

GUERMOUCHE, Abdou
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithms and high performance computing for grand challenge applications [SCALAPPLIX]

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Communication dans un congrès

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Proceedings of High Performance Computing – HiPC 2007, Proceedings of High Performance Computing – HiPC 2007, High Performance Computing – HiPC 2007, 2007. 2007p. 47-58

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High performance sparse direct solvers are often a method of choice in various simulation problems. However, they require a large amount of memory compared to iterative methods. In this context, out-of-core solvers must be employed, where disks are used when the storage requirements are too large with respect to the physical memory available. In this paper, we study how to minimize the I/O requirements in the multifrontal method, a particular direct method to solve large-scale problems efficiently. Experiments on large real-life problems also show that the volume of I/O obtained when minimizing the storage requirement can be significantly reduced by applying algorithms designed to reduce the I/O volume.< Réduire

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Reducing the I/O Volume in an Out-of-Core Sparse Multifrontal Solver

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