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Create 2024-12-19-implementation-of-two-numerical-solvers-for-the-study-of-non-equilibrium-gas-dynamics-on-gpu-accelerated-platforms-using-sycl.md
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---
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contributor: max
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date: '2024-12-19T09:43:10'
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title: 'Implementation of Two Numerical Solvers for the Study of Non-Equilibrium Gas Dynamics on GPU-Accelerated Platforms using SYCL'
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external_url: 'https://ruor.uottawa.ca/items/cb39b8e3-9904-4a65-89bf-5414d364e759'
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authors:
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- El-Ghotmi, Osman
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tags:
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- sycl
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- gpu
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- portability
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---
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The application of GPUs has extended beyond traditional graphics rendering because their
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parallel processing capabilities can accelerate many general-purpose tasks, such as machine
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learning and scientific computing. This thesis presents the implementation of two numerical
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solvers for the solution of non-equilibrium gas flows. It also demonstrates the computational
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performance of the two solvers when developed to target GPU-based supercomputers using the SYCL
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programming model. The first solver incorporates a novel ray-tracing technique and accurate
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mathematical relations to efficiently compute any observable property of free-molecular flow
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past convex shapes (FMFC). It computes integrals of the Maxwell-Boltzmann distribution function
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to create an algorithm that quickly evaluates any moment of the local particle-velocity
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distribution. This highly efficient technique is extended for GPUs to accelerate the
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computation of accurate results. Results produced with the solver serve as robust benchmarks
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in the validation of other scientific models that describe fluid motion in non-equilibrium
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regimes. The second solver extends a CPU-based implementation of the discontinuous Galerkin Hancock (DGH)
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method into an efficient GPU code. The DGH scheme is a high-order numerical method that
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solves hyperbolic partial differential equations (PDEs) with stiff source terms. This class
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of equations is common in many models that are used to describe non-equilibrium gas flows.
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The GPU implementation of the DGH solver that is presented in this work provides a
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computationally efficient and numerically accurate method to compute the solution for these
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models. Results produced by the FMFC and DGH solvers showcase their accuracy and parallel
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scalability as efficient GPU algorithms. Furthermore, the effectiveness of the FMFC
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solver as a validation tool is demonstrated by producing benchmarks to confirm the
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accuracy of scientific models that are solved with numerical schemes such as DGH.

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