Abstract
The effect of two soft fault error models on the convergence of the parallel flexible GMRES (FGMRES) iterative method solving an elliptical PDE problem on a regular grid is evaluated. We consider two types of preconditioners: an incomplete LU factorization with dual threshold (ILUT), and an algebraic recursive multilevel solver (ARMS) combined with random butterfly transformation (RBT). The experiments quantify the difference between two soft fault error models considered in this study and compare their potential impact on the convergence.
This work was supported in part by the Air Force Office of Scientific Research under the AFOSR award FA9550-12-1-0476 by the U.S. Department of Energy, Office of Advanced Scientific Computing Research, through the Ames Laboratory, operated by Iowa State University under contract No. DE-AC02-07CH11358, and by the U.S. Department of Defense High Performance Computing Modernization Program, through a HASI grant, and the ILIR/IAR program at NSWC Dahlgren. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and of Old Dominion University operating the Turing High Performance Computing Cluster.
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Coleman, E., Jamal, A., Baboulin, M., Khabou, A., Sosonkina, M. (2018). A Comparison of Soft-Fault Error Models in the Parallel Preconditioned Flexible GMRES. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10777. Springer, Cham. https://doi.org/10.1007/978-3-319-78024-5_4
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