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## Expected time-steps/hour
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The following table outlines observed performance as nanoseconds per grid point (ns/GP) per equation (eq) per right-hand side (rhs) evaluation (lower is better).
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We solve an example 3D, inviscid, 5-equation model problem with two advected species (a total of 8 PDEs).
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The numerics are WENO5 and the HLLC approximate Riemann solver.
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We solve an example 3D, inviscid, 5-equation model problem with two advected species (8 PDEs) and 8M grid points (158-cubed uniform grid).
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The numerics are WENO5 finite volume reconstruction and HLLC approximate Riemann solver.
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This case is located in `examples/3D_performance_test`.
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We report results for various numbers of grid points per CPU die (or GPU device) and hardware.
| Apple M2 | 6 cores | 365 | 306 | 563 | GNU 13.2.0 | N/A |
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__All results are in nanoseconds (ns) per grid point (gp) per equation (eq) per right-hand side (rhs) evaluation, so X ns/gp/eq/rhs. Lower is better.__
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Results are for MFC v4.9.3 (July 2024 release), though numbers have not changed meaningfully since then.
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All results are for the compiler that gave the best performance.
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CPU results may be performed on CPUs with more cores than reported in the table; we report results for the best performance given the full processor die by checking the performance for different core counts on that device.
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GPU results on single-precision (SP) GPUs performed computation in double-precision via conversion in compiler/software; these numbers are _not_ for single-precision computation.
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AMD MI250X GPUs have two graphics compute dies (GCDs) per MI250X device; we report results for one GCD, though one can quickly estimate full MI250X runtime by halving the single GCD grind time number.
__All grind times are in nanoseconds (ns) per grid point (gp) per equation (eq) per right-hand side (rhs) evaluation, so X ns/gp/eq/rhs. Lower is better.__
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