Výsledky vyhledávání - approximate sparse triangular solves

  1. 1

    Incomplete Sparse Approximate Inverses for Parallel Preconditioning Autor Anzt, Hartwig, Huckle, Thomas K., Bräckle, Jürgen, Dongarra, Jack

    ISSN: 0167-8191, 1872-7336
    Vydáno: Elsevier B.V 01.01.2018
    Vydáno v Parallel computing (01.01.2018)
    “… The “Incomplete Sparse Approximate Inverses” (ISAI) is in particular efficient in the solution of sparse triangular linear systems of equations…”
    Získat plný text
    Journal Article
  2. 2

    Incomplete Sparse Approximate Inverses for Parallel Preconditioning Autor Anzt, Hartwig, Huckle, Thomas K., Bräckle, Jürgen, Dongarra, Jack

    ISSN: 0167-8191, 1872-7336
    Vydáno: United States Elsevier 28.10.2017
    Vydáno v Parallel computing (28.10.2017)
    “… The “Incomplete Sparse Approximate Inverses” (ISAI) is in particular efficient in the solution of sparse triangular linear systems of equations…”
    Získat plný text
    Journal Article
  3. 3

    A Comparative Study of Block Incomplete Sparse Approximate Inverses Preconditioning on Tesla K20 and V100 GPUs Autor Ma, Wenpeng, Yuan, Wu, Liu, Xiazhen

    ISSN: 1999-4893, 1999-4893
    Vydáno: Basel MDPI AG 01.07.2021
    Vydáno v Algorithms (01.07.2021)
    “…Incomplete Sparse Approximate Inverses (ISAI) has shown some advantages over sparse triangular solves on GPUs when it is used for the incomplete LU based preconditioner…”
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    Journal Article
  4. 4

    Using Jacobi iterations and blocking for solving sparse triangular systems in incomplete factorization preconditioning Autor Chow, Edmond, Anzt, Hartwig, Scott, Jennifer, Dongarra, Jack

    ISSN: 0743-7315, 1096-0848
    Vydáno: United States Elsevier Inc 01.09.2018
    “…When using incomplete factorization preconditioners with an iterative method to solve large sparse linear systems, each application of the preconditioner involves solving two sparse triangular systems…”
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    Journal Article
  5. 5

    Hierarchical Orthogonal Factorization: Sparse Least Squares Problems Autor Gnanasekaran, Abeynaya, Darve, Eric

    ISSN: 0885-7474, 1573-7691
    Vydáno: New York Springer US 01.05.2022
    Vydáno v Journal of scientific computing (01.05.2022)
    “…), that was developed by the authors to solve large sparse linear systems. Our algorithm is built on top of a Nested Dissection based multifrontal QR approach…”
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    Journal Article
  6. 6

    Structure-adaptive parallel solution of sparse triangular linear systems Autor Totoni, Ehsan, Heath, Michael T., Kale, Laxmikant V.

    ISSN: 0167-8191, 1872-7336
    Vydáno: Elsevier B.V 01.10.2014
    Vydáno v Parallel computing (01.10.2014)
    “…•We develop a novel parallel algorithm for solution of sparse triangular linear systems…”
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    Journal Article
  7. 7

    Batched Generation of Incomplete Sparse Approximate Inverses on GPUs Autor Anzt, Hartwig, Chow, Edmond, Huckle, Thomas, Dongarra, Jack

    Vydáno: IEEE 01.11.2016
    “…Incomplete Sparse Approximate Inverses (ISAI) have recently been shown to be an attractive alternative to exact sparse triangular solves in the context of incomplete factorization preconditioning…”
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    Konferenční příspěvek
  8. 8

    An Experimental Study of Two-level Schwarz Domain-Decomposition Preconditioners on GPUs Autor Yamazaki, Ichitaro, Heinlein, Alexander, Rajamanickam, Sivasankaran

    ISSN: 1530-2075
    Vydáno: IEEE 01.05.2023
    “… When used to accelerate the convergence rate of Krylov subspace iterative methods, the GDSW preconditioner provides robustness and scalability for the solution of sparse linear systems arising…”
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    Konferenční příspěvek
  9. 9
  10. 10

    A Second-Order Triangular Element-Based Domain Decomposition Method for Solving Electromagnetic Scattering Problems Using the Surface Integral Equation Autor Fan, Chaoqun, Wei, Gao, Zhu, Yi, Liu, Xiangwei, Li, Jianzhou

    ISSN: 0018-926X, 1558-2221
    Vydáno: New York IEEE 01.08.2025
    “… Distinct processing methods are applied to each domain and an approximate sparse and locally low-rank impedance matrix is created…”
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    Journal Article
  11. 11

    Batched generation of incomplete sparse approximate inverses on GPUs Autor Anzt, Hartwig, Chow, Edmond, Huckle, Thomas, Dongarra, Jack

    ISBN: 1509052224, 9781509052226
    Vydáno: Piscataway, NJ, USA IEEE Press 13.11.2016
    “…Incomplete Sparse Approximate Inverses (ISAI) have recently been shown to be an attractive alternative to exact sparse triangular solves in the context of incomplete factorization preconditioning…”
    Získat plný text
    Konferenční příspěvek
  12. 12

    Sparse Network Asymptotics for Logistic Regression Under Possible Misspecification Autor Graham, Bryan S.

    ISSN: 0012-9682, 1468-0262
    Vydáno: Evanston Blackwell Publishing Ltd 01.11.2024
    Vydáno v Econometrica (01.11.2024)
    “… Condition (ii) implies that the limiting network of purchases is sparse: only a vanishing fraction of all possible purchases are actually…”
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    Journal Article
  13. 13

    Estimating the parallelism in the solution of sparse triangular linear systems Autor Gonzalez, Eduardo, Dufrechou, Ernesto, Ezzatti, Pablo

    Vydáno: IEEE 16.11.2020
    “…Most direct methods to solve sparse linear systems, as well as preconditioners for iterative methods, factorize the problem to operate on sparse triangular matrices…”
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    Konferenční příspěvek
  14. 14

    Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations Autor Cohen, Michael B., Kelner, Jonathan, Kyng, Rasmus, Peebles, John, Peng, Richard, Rao, Anup B., Sidford, Aaron

    ISSN: 2575-8454
    Vydáno: IEEE 01.10.2018
    “…In this paper, we show how to solve directed Laplacian systems in nearly-linear time…”
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    Konferenční příspěvek
  15. 15

    Factored approximate inverse preconditioners with dynamic sparsity patterns Autor Lee, Eun-Joo, Zhang, Jun

    ISSN: 0898-1221, 1873-7668
    Vydáno: Elsevier Ltd 01.07.2011
    “…We propose two sparsity pattern selection algorithms for factored approximate inverse preconditioners to solve general sparse matrices…”
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    Journal Article
  16. 16

    Hierarchical Orthogonal Factorization: Sparse Least Squares Problems Autor Gnanasekaran, Abeynaya, Darve, Eric

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 04.03.2021
    Vydáno v arXiv.org (04.03.2021)
    “… We build upon the algorithm, spaQR (sparsified QR), that was developed by the authors to solve large sparse linear systems…”
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    Paper
  17. 17

    Hierarchical Orthogonal Factorization: Sparse Square matrices Autor Gnanasekaran, Abeynaya, Darve, Eric

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 14.10.2020
    Vydáno v arXiv.org (14.10.2020)
    “…In this work, we develop a new fast algorithm, spaQR -- sparsified QR, for solving large, sparse linear systems…”
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    Paper
  18. 18

    A Comparative Study of Preconditioners for GPU-Accelerated Conjugate Gradient Solver Autor Yao Chen, Yonghua Zhao, Wei Zhao, Lian Zhao

    Vydáno: IEEE 01.11.2013
    “… For the standard IC preconditioner, we exploit level scheduling to increase multi-thread parallelism of sparse triangular solve on GPU…”
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    Konferenční příspěvek
  19. 19

    An Experimental Study of Two-Level Schwarz Domain Decomposition Preconditioners on GPUs Autor Yamazaki, Ichitaro, Heinlein, Alexander, Rajamanickam, Sivasankaran

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 10.04.2023
    Vydáno v arXiv.org (10.04.2023)
    “… When used to accelerate the convergence rate of Krylov subspace iterative methods, the GDSW preconditioner provides robustness and scalability for the solution of sparse linear systems arising…”
    Získat plný text
    Paper
  20. 20

    Preconditioning Techniques for Sparse Linear Systems Autor Regev, Shaked

    ISBN: 9798374478044
    Vydáno: ProQuest Dissertations & Theses 01.01.2022
    “…We present two novel methods, SSAI and HyKKT, for sparse linear systems. The methods differ in that SSAI is meant to be an out of the box solver that is robust on many different types of Hermitian positive definite (HPD…”
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    Dissertation