AmgX: A Library for GPU Accelerated Algebraic Multigrid and Preconditioned Iterative Methods
| Autor: | M. Arsaev, Joe Eaton, Jonathan Cohen, N. Markovskiy, Julien Demouth, Simon K. Layton, V. Sellappan, Maxim Naumov, Robert Strzodka, Patrice Castonguay, Istvan Z. Reguly, Nikolai Sakharnykh |
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| Rok vydání: | 2015 |
| Předmět: |
Iterative method
business.industry Computer science Applied Mathematics Linear system MathematicsofComputing_NUMERICALANALYSIS Krylov subspace Computational fluid dynamics Computer Science::Numerical Analysis Mathematics::Numerical Analysis Computational science Computational Mathematics CUDA Multigrid method Factorization Computer Science::Mathematical Software business Interpolation |
| Zdroj: | SIAM Journal on Scientific Computing. 37:S602-S626 |
| ISSN: | 1095-7197 1064-8275 |
| Popis: | The solution of large sparse linear systems arises in many applications, such as computational fluid dynamics and oil reservoir simulation. In realistic cases the matrices are often so large that they require large scale distributed parallel computing to obtain the solution of interest in a reasonable time. In this paper we discuss the design and implementation of the AmgX library, which provides drop-in GPU acceleration of distributed algebraic multigrid (AMG) and preconditioned iterative methods. The AmgX library implements both classical and aggregation-based AMG methods with different selector and interpolation strategies, along with a variety of smoothers and preconditioners, including block-Jacobi, Gauss--Seidel, and incomplete-LU factorization. The library contains many of the standard and flexible preconditioned Krylov subspace iterative methods, which can be combined with any of the available multigrid methods or simpler preconditioners. The parallelism in the aggregation scheme exploits parallel... |
| Databáze: | OpenAIRE |
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