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Algorithm-based fault tolerance for dense matrix factorizations
2012
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming - PPoPP '12
Dense matrix factorizations, like LU, Cholesky and QR, are widely used for scientific applications that require solving systems of linear equations, eigenvalues and linear least squares problems. Such computations are normally carried out on supercomputers, whose ever-growing scale induces a fast decline of the Mean Time To Failure (MTTF). This paper proposes a new hybrid approach, based on Algorithm-Based Fault Tolerance (ABFT), to help matrix factorizations algorithms survive fail-stop
doi:10.1145/2145816.2145845
dblp:conf/ppopp/DuBBHD12
fatcat:cyc73fwdtvhhve7gjzi6vyc7ne