A GPU parallel randomized CUR compression method for the Method of Moments
In this work, we propose a GPU parallel implementation of the randomized CUR (or Pseudo Skeleton) Approximation to compress the -matrices of linear systems that arise in the discretization of integral equations modeling electromagnetic scattering problems. This compression method is highly paralleli...
| Autores: | ; ; ; |
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| Tipo de documento: | Artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/391415 |
| Acceso en línea: | https://hdl.handle.net/2117/391415 https://dx.doi.org/10.1016/j.cpc.2023.108696 |
| Nivel de acceso: | Acceso abierto |
| Palabra clave: | Estimation theory Electric field integral equation Graphics processing unit Low-rank approximation Method of Moments H-matrices Randomized Estimació, Teoria de l' Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Sumario: | In this work, we propose a GPU parallel implementation of the randomized CUR (or Pseudo Skeleton) Approximation to compress the -matrices of linear systems that arise in the discretization of integral equations modeling electromagnetic scattering problems. This compression method is highly parallelizable, in contrast with other similar methods such as the Adaptive Cross Approximation. It involves dense linear algebra computations that can be efficiently implemented on a GPU device. Besides, a stochastic convergence criterion is introduced to minimize the communication between the host and the device. Testing the code with standard cases shows the efficiency and accuracy of the method. |
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