Iakymchuk, Roman; Barreda, Maria; Wiesenberger, Matthias; Aliaga, José I.; Quintana-Ortí, Enrique S. Reproducibility strategies for parallel preconditioned conjugate gradient. (English) Zbl 1433.65368 J. Comput. Appl. Math. 371, Article ID 112697, 13 p. (2020). Summary: The Preconditioned Conjugate Gradient method is often used in numerical simulations. While being widely used, the solver is also known for its lack of accuracy while computing the residual. In this article, we aim at a twofold goal: enhance the accuracy of the solver but also ensure its reproducibility in a message-passing implementation. We design and employ various strategies starting from the ExBLAS approach (through preserving every bit of information until final rounding) to its more lightweight performance-oriented variant (through expanding the intermediate precision). These algorithmic strategies are reinforced with programmability suggestions to assure deterministic executions. Finally, we verify these strategies on modern HPC systems: both versions deliver reproducible number of iterations, residuals, direct errors, and vector-solutions for the overhead of only 29% (ExBLAS) and 4% (lightweight) on 768 processes. Cited in 3 Documents MSC: 65Y05 Parallel numerical computation 65Y20 Complexity and performance of numerical algorithms 68Q10 Modes of computation (nondeterministic, parallel, interactive, probabilistic, etc.) Keywords:reproducibility; accuracy; floating-point expansion; long accumulator; preconditioned conjugate gradient; high-performance computing Software:BLAS; mctoolbox; MPFR; LBNL; FELTOR; HPL; ExBLAS PDFBibTeX XMLCite \textit{R. Iakymchuk} et al., J. Comput. Appl. Math. 371, Article ID 112697, 13 p. (2020; Zbl 1433.65368) Full Text: DOI HAL References: [1] Barrett, R.; Berry, M.; Chan, T. F.; Demmel, J.; Donato, J.; Dongarra, J.; Eijkhout, V.; Pozo, R.; Romine, C.; der Vorst, H. 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