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<p>Reference: Bezgin, D. A., & Buhendwa A. B., & Adams N. A. (2022). JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows. arXiv:2203.13760</p>
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<p>Reference: Ghia, U., & Ghia, K. N., & Shin, C. T. (1982). High-re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method. Journal of Computational Physics, 48, 387-411</p>
<p>Reference: Hillewaert, K. (2013). TestCase C3.5 - DNS of the transition of the Taylor-Green vortex, Re=1600 - Introduction and result summary. 2nd International Workshop on high-order methods for CFD.</p>
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Final Condition</h2>
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<p>This figure shows the isosurface with zero q-criterion. <imgsrc="result-3D_TaylorGreenVortex-example.png" alt="" class="inline" title="Density"/></p>
<p>Reference: Hillewaert, K. (2013). TestCase C3.5 - DNS of the transition of the Taylor-Green vortex, Re=1600 - Introduction and result summary. 2nd International Workshop on high-order methods for CFD.</p>
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Final Condition</h2>
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<p>This figure shows the isosurface with zero q-criterion. <imgsrc="result-3D_TaylorGreenVortex-example.png" alt="" class="inline" title="Density"/></p>
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<h1><aclass="anchor" id="autotoc_md36"></a>
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Shu-Osher problem (1D)</h1>
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<p>Reference: C. W. Shu, S. Osher, Efficient implementation of essentially non-oscillatory shock-capturing schemes, Journal of Computational Physics 77 (2) (1988) 439–471. doi:10.1016/0021-9991(88)90177-5.</p>
<p><imgsrc="linear_theory.jpg" alt="Linear Theory Comparison" class="inline"/></p>
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<h1><aclass="anchor" id="autotoc_md39"></a>
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Strong- & Weak-scaling</h1>
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<p>The <ahref="case.py"><b>Scaling</b></a> case can exercise both weak- and strong-scaling. It adjusts itself depending on the number of requested ranks.</p>
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<p>This directory also contains a collection of scripts used to test strong-scaling on OLCF Frontier. They required modifying MFC to collect some metrics but are meant to serve as a reference to users wishing to run similar experiments.</p>
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<h2><aclass="anchor" id="autotoc_md40"></a>
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Weak Scaling</h2>
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<p>Pass <code>--scaling weak</code>. The <code>--memory</code> option controls (approximately) how much memory each rank should use, in Gigabytes. The number of cells in each dimension is then adjusted according to the number of requested ranks and an approximation for the relation between cell count and memory usage. The problem size increases linearly with the number of ranks.</p>
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<h2><aclass="anchor" id="autotoc_md41"></a>
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Strong Scaling</h2>
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<p>Pass <code>--scaling strong</code>. The <code>--memory</code> option controls (approximately) how much memory should be used in total during simulation, across all ranks, in Gigabytes. The problem size remains constant as the number of ranks increases.</p>
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Example</h2>
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<p>For example, to run a weak-scaling test that uses ~4GB of GPU memory per rank on 8 2-rank nodes with case optimization, one could:</p>
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<divclass="fragment"><divclass="line">./mfc.sh run examples/scaling/case.py -t pre_process simulation \</div>
</div><!-- fragment --><h1><aclass="anchor" id="autotoc_md43"></a>
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2D Riemann Test (2D)</h1>
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<p>Reference: Chamarthi, A., & Hoffmann, N., & Nishikawa, H., & Frankel S. (2023). Implicit gradients based conservative numerical scheme for compressible flows. arXiv:2110.05461</p>
<p>Reference: V. A. Titarev, E. F. Toro, Finite-volume WENO schemes for three-dimensional conservation laws, Journal of Computational Physics 201 (1) (2004) 238–260.</p>
<p>Reference: C. W. Shu, S. Osher, Efficient implementation of essentially non-oscillatory shock-capturing schemes, Journal of Computational Physics 77 (2) (1988) 439–471. doi:10.1016/0021-9991(88)90177-5.</p>
<p>The <ahref="case.py"><b>Scaling</b></a> case can exercise both weak- and strong-scaling. It adjusts itself depending on the number of requested ranks.</p>
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<p>This directory also contains a collection of scripts used to test strong-scaling on OLCF Frontier. They required modifying MFC to collect some metrics but are meant to serve as a reference to users wishing to run similar experiments.</p>
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<h2><aclass="anchor" id="autotoc_md49"></a>
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Weak Scaling</h2>
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<p>Pass <code>--scaling weak</code>. The <code>--memory</code> option controls (approximately) how much memory each rank should use, in Gigabytes. The number of cells in each dimension is then adjusted according to the number of requested ranks and an approximation for the relation between cell count and memory usage. The problem size increases linearly with the number of ranks.</p>
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<h2><aclass="anchor" id="autotoc_md50"></a>
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Strong Scaling</h2>
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<p>Pass <code>--scaling strong</code>. The <code>--memory</code> option controls (approximately) how much memory should be used in total during simulation, across all ranks, in Gigabytes. The problem size remains constant as the number of ranks increases.</p>
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Example</h2>
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<p>For example, to run a weak-scaling test that uses ~4GB of GPU memory per rank on 8 2-rank nodes with case optimization, one could:</p>
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<divclass="fragment"><divclass="line">./mfc.sh run examples/scaling/case.py -t pre_process simulation \</div>
<p>Reference: Bezgin, D. A., & Buhendwa A. B., & Adams N. A. (2022). JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows. arXiv:2203.13760</p>
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<p>Reference: Ghia, U., & Ghia, K. N., & Shin, C. T. (1982). High-re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method. Journal of Computational Physics, 48, 387-411</p>
<p>Reference: P. D. Lax, Weak solutions of nonlinear hyperbolic equations and their numerical computation, Communications on pure and applied mathematics 7 (1) (1954) 159–193.</p>
<p>Reference: Chamarthi, A., & Hoffmann, N., & Nishikawa, H., & Frankel S. (2023). Implicit gradients based conservative numerical scheme for compressible flows. arXiv:2110.05461</p>
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