Parallel Computing 38(1), 37–51 (2012)ĭongarra, J., Faverge, M., Herault, T., Jacquelin, M., Langou, J., Robert, Y.: Hierarchical QR factorization algorithms for multi-core clusters. In: IPDPS (2008)īosilca, G., Bouteiller, A., Danalis, A., Herault, T., Lemarinier, P., Dongarra, J.: DAGuE: A generic distributed DAG engine for high performance computing. The National Institute for Computational Sciences: Kraken machine size, īhatele, A., Kale, L.V.: Application-specific topology-aware mapping for three dimensional topologies. This process is experimental and the keywords may be updated as the learning algorithm improves.Īdiga, N.R., Almási, G., Almasi, G.S., Aridor, Y., Barik, R., Beece, D., Bellofatto, R., et al.: An overview of the BlueGene/L supercomputer. These keywords were added by machine and not by the authors. We show that the new algorithm exhibits competitive performance with state-of-the-art QR routines on a supercomputer called Kraken, which shows that high-level programming environments, such as PaRSEC, provide a viable alternative to enhance the production of quality software on complex and hierarchical architectures. The complexity of the implementation is addressed with the PaRSEC software, which takes as input a parametrized dependence graph, which is derived from the algorithm, and only requires the user to decide, at the high-level, the allocation of tasks to nodes. The implementation of the algorithm uses threads at the node level, and MPI for internode communications. ![]() The algorithm targets a virtual 3D-array and requires only local communications. This article introduces a new systolic algorithm for QR factorization, and its implementation on a supercomputing cluster of multicore nodes.
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