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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>The <ahref="case.py"><b>3D_weak_scaling</b></a> case depends on two parameters:</p>
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<ul>
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<li><b>The number of MPI ranks</b> (<em>procs</em>): As <em>procs</em> increases, the problem size per rank remains constant. <em>procs</em> is determined using information provided to the case file by <code>mfc.sh run</code>.</li>
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<li><b>GPU memory usage per rank</b> (<em>gbpp</em>): As <em>gbpp</em> increases, the problem size per rank increases and the number of timesteps decreases so that wall times consistent. <em>gbpp</em> is a user-defined optional argument to the <ahref="case.py">case.py</a> file. It can be specified right after the case filepath when invoking <code>mfc.sh run</code>.</li>
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</ul>
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<p>Weak scaling benchmarks can be produced by keeping <em>gbpp</em> constant and varying <em>procs</em>.</p>
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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/3D_weak_scaling/case.py 4 -t pre_process simulation \</div>
</div><!-- fragment --><h1><aclass="anchor" id="autotoc_md41"></a>
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Lax shock tube problem (1D)</h1>
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<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>
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<h2><aclass="anchor" id="autotoc_md42"></a>
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<h1><aclass="anchor" id="autotoc_md43"></a>
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Titarev-Toro problem (1D)</h1>
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<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: 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>The <ahref="case.py"><b>3D_weak_scaling</b></a> case depends on two parameters:</p>
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<ul>
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<li><b>The number of MPI ranks</b> (<em>procs</em>): As <em>procs</em> increases, the problem size per rank remains constant. <em>procs</em> is determined using information provided to the case file by <code>mfc.sh run</code>.</li>
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<li><b>GPU memory usage per rank</b> (<em>gbpp</em>): As <em>gbpp</em> increases, the problem size per rank increases and the number of timesteps decreases so that wall times consistent. <em>gbpp</em> is a user-defined optional argument to the <ahref="case.py">case.py</a> file. It can be specified right after the case filepath when invoking <code>mfc.sh run</code>.</li>
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</ul>
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<p>Weak scaling benchmarks can be produced by keeping <em>gbpp</em> constant and varying <em>procs</em>.</p>
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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/3D_weak_scaling/case.py 4 -t pre_process simulation \</div>
</div><!-- fragment --><h1><aclass="anchor" id="autotoc_md50"></a>
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Lax shock tube problem (1D)</h1>
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<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>
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['1d_4',['1D',['../md_examples.html#autotoc_md50',1,'Lax shock tube problem (1D)'],['../md_examples.html#autotoc_md40',1,'Shu-Osher problem (1D)'],['../md_examples.html#autotoc_md43',1,'Titarev-Toro problem (1D)']]]
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