Title:
COMET-GPU: A GPGPU-Enabled Deterministic Solver for the Continuous-Energy Coarse Mesh Transport Method (COMET)
COMET-GPU: A GPGPU-Enabled Deterministic Solver for the Continuous-Energy Coarse Mesh Transport Method (COMET)
Author(s)
Burke, Paul
Advisor(s)
Rahnema, Farzad
Çatalyürek, Ümit V.
Çatalyürek, Ümit V.
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Abstract
The Continuous-Energy Coarse Mesh Transport (COMET) method is a neutron transport solution method that uses a unique hybrid stochastic-deterministic solution method to obtain high-fidelity whole-core solutions to reactor physics problems with formidable speed. This method involves pre-computing solutions to individual coarse meshes within the global problem, then using a deterministic transport sweep to construct a whole-core solution from these local solutions. In this work, a new implementation of the deterministic transport sweep solver is written which includes the ability to accelerate the calculation using up to 4 Graphics Processing Units (GPUs) on one computational node. To demonstrate the new implementation, three whole-core benchmark problems were solved using the previous serial solver and various configurations of the new solver, with the relative performance compared. In this comparison, it was found that the application of one GPU to the problem resulted in between a 100x-150x speedup (depending on the specific problem) relative to the old serial solver. Excellent scaling up to 4 GPUs was observed, which brought the total speedup up to 450x-500x. As an example of a new type of analysis which is enabled by the improved speed of the solver, a sensitivity study was performed on the convergence thresholds used in the inner and outer iteration processes. This study involves repeatedly solving problems using slightly varying thresholds, including computing a “gold-standard” solution to double-precision. These various runs would be prohibitively expensive if run using the old solver but in this work were completed in around an hour.
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Date Issued
2021-04-28
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Text
Resource Subtype
Dissertation