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Reproducibility strategies for parallel preconditioned Conjugate Gradient

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Reproducibility strategies for parallel preconditioned Conjugate Gradient

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dc.contributor.author Iakymchuk, Roman es_ES
dc.contributor.author Barreda, María es_ES
dc.contributor.author Wiesenberger, Matthias es_ES
dc.contributor.author Aliaga, José I. es_ES
dc.contributor.author Quintana Ortí, Enrique Salvador es_ES
dc.date.accessioned 2021-07-24T03:33:36Z
dc.date.available 2021-07-24T03:33:36Z
dc.date.issued 2020-06 es_ES
dc.identifier.issn 0377-0427 es_ES
dc.identifier.uri http://hdl.handle.net/10251/170081
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship To begin with, we would like to thank the reviewers for their thorough reading of the article as well as their valuable comments and suggestions. This research was partially supported by the European Union's Horizon 2020 research, innovation programme under the Marie Sklodowska-Curie grant agreement via the Robust project No. 842528 as well as the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the H2020 EC RIA Programme; in particular, the author gratefully acknowledges the support of Vicenc Beltran and the computer resources and technical support provided by BSC. The researchers from Universitat Jaume I (UJI) and Universidad Politecnica de Valencia (UPV) were supported by MINECO, Spain project TIN2017-82972-R. Maria Barreda was also supported by the POSDOC-A/2017/11 project from the Universitat Jaume I, Spain. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Computational and Applied Mathematics es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Reproducibility es_ES
dc.subject Accuracy es_ES
dc.subject Floating-point expansion es_ES
dc.subject Long accumulator es_ES
dc.subject Preconditioned Conjugate Gradient es_ES
dc.subject High-Performance Computing es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Reproducibility strategies for parallel preconditioned Conjugate Gradient es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cam.2019.112697 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/730897/EU/Transnational Access Programme for a Pan-European Network of HPC Research Infrastructures and Laboratories for scientific computing/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UJI//POSDOC-A%2F2017%2F11/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/842528/EU/Robust and Energy-Efficient Numerical Solvers Towards Reliable and Sustainable Scientific Computations/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-82972-R/ES/TECNICAS ALGORITMICAS PARA COMPUTACION DE ALTO RENDIMIENTO CONSCIENTE DEL CONSUMO ENERGETICO Y RESISTENTE A ERRORES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Iakymchuk, R.; Barreda, M.; Wiesenberger, M.; Aliaga, JI.; Quintana Ortí, ES. (2020). Reproducibility strategies for parallel preconditioned Conjugate Gradient. Journal of Computational and Applied Mathematics. 371:1-13. https://doi.org/10.1016/j.cam.2019.112697 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.cam.2019.112697 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 371 es_ES
dc.relation.pasarela S\401226 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Universitat Jaume I es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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