國立虎尾科技大學 |

Hardware Awareness for the Selection of Optimal Iterative Linear Solvers.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Hardware Awareness for the Selection of Optimal Iterative Linear Solvers./
作者:	Motter, Pate Allen.
面頁冊數:	1 online resource (160 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355542936

Hardware Awareness for the Selection of Optimal Iterative Linear Solvers.
Motter, Pate Allen.

Hardware Awareness for the Selection of Optimal Iterative Linear Solvers. - 1 online resource (160 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2017.

Includes bibliographical references

Solving sparse systems of linear equations is a commonly encountered computation in scientific and high-performance computing applications. Applications that depend on solving sparse linear systems as part of their workflow can spend a large percentage of their total runtime solving sparse systems. However, selecting the best iterative solver and preconditioner for solving a given sparse linear system, especially for novice users, is not a simple task. To address this problem, previous works have used machine learning techniques to find similarities between sparse matrices and the corresponding performance that solver-preconditioner pairs have on solving the resulting linear systems. This dissertation expands on existing work by introducing new techniques that incorporate hardware information into the prediction of ideal iterative linear solver and preconditioners for sparse linear systems. By accounting for hardware, it is possible to create more specially tailored solver-preconditioner recommendations for a novice user.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355542936Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Hardware Awareness for the Selection of Optimal Iterative Linear Solvers.
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500 $a Adviser: Elizabeth Jessup.
502 $a Thesis (Ph.D.)--University of Colorado at Boulder, 2017.
504 $a Includes bibliographical references
520 $a Solving sparse systems of linear equations is a commonly encountered computation in scientific and high-performance computing applications. Applications that depend on solving sparse linear systems as part of their workflow can spend a large percentage of their total runtime solving sparse systems. However, selecting the best iterative solver and preconditioner for solving a given sparse linear system, especially for novice users, is not a simple task. To address this problem, previous works have used machine learning techniques to find similarities between sparse matrices and the corresponding performance that solver-preconditioner pairs have on solving the resulting linear systems. This dissertation expands on existing work by introducing new techniques that incorporate hardware information into the prediction of ideal iterative linear solver and preconditioners for sparse linear systems. By accounting for hardware, it is possible to create more specially tailored solver-preconditioner recommendations for a novice user.
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