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OpenMG: A New Multigrid Implementation in Python
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scipy_2012
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Author(s):
Tom Bertalan, Akand W. Islam
Location
106
Date
jul Wed 18
Days Raw Files
Start
11:55
First Raw Start
11:54
Duration
20:00
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0:00:25
End
12:15
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12:15
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00:00
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20 min.
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Here, include a talk summary of no longer than 500 words. Aspects such as relevance to Python in science, applicability, and novelty will be considered by the program committee. In most large-scale computations, systems of equations arise in the form Au=b, where A is a linear operation to be performed on the unknown data u, producing the known right-hand-side, b, which represents some constraint of known or assumed behavior of the system being modeled. Since u can have a many millions to billions elements, direct solution is too slow. A multigrid solver solves partially at full resolution, and then solves directly only at low resolution. This creates a correction vector, which is then interpolated to full resolution, where it corrects the partial solution. This project aims to create an open-source multigrid solver library, written only in Python. The existing PyAMG multigrid implementation–a highly versatile, highly configurable, black-box solver–is fully sequential, and is difficult to read and modify due to its C core. OpenMG is a pure Python experimentation environment for developing multigrid optimizations, not a new production solver library. By making the code simple and modular, we make the alogrithmic details clear. We thereby create an opportunity for education and experimental optimization of the partial solver (Jacobi, Gauss Seidel, SOR, etc.), the restriction mechanism, the prolongation mechanism, and the direct solver, using GPGPU, multiple CPUs, MPI, or grid computing. The resulting solver is tested on an implicit pressure reservoir simulation problem with satisfactory results.
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production notes
2012-07-18/09_54_35.dv
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11:54:35 - 12:15:12 ( 00:20:37 )
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