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Silent Data Corruption Resilient Matrix Factorizations on Distributed Memory System


The lack of efficient resilience solutions is expected to be a major problem for the coming exascale supercomputers, as the chance that a long running large scale computation can finish without faults is diminishing quickly. In this dissertation I try to develop algorithmic techniques to provide fault tolerance for the commonly used matrix factorization algorithms and its high performance implementation in distributed memory massively parallel systems, with very low overhead and high scalability.

Specifically, I design numerical error correcting encoding of matrix and the corresponding algorithms to tolerate hardware faults during matrix factorizations. It is in common with error correcting codes (ECC) used widely in communication and storage systems that use codes to detect and correct errors occured during communication or at rest in storage cells. The salient difference is that while ECC protects invariable data, I need an ECC for variable matrix that is under factorization. My previous and current work covers the design of such algorithmic fault tolerance techniques for the six most widely used matrix factorizations — LU, QR, Cholesky, SVD, Hessenberg reduction, and tridiagonal reduction which comprise the core functionality of the de facto dense linear algebra package ScaLAPACK (Scalable Linear Algebra PACKage). The novel approach I used extensively is the on-line ABFT which not only designs the numerical codes but also modifies the algorithm to maintain the checksum in flight. For LU/QR/Cholesky factorizations, the on-line transformation results in vastly improved fault tolerance at a small extra cost. For SVD/Hessenberg/tridiagonal factorizations where no ABFT exist, the on-line ABFT fills this void and produces similarly highly scalable, resilient, and efficient algorithms and implementations.

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