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In this article, cache blocking is implemented for the Navier Stokesequations with anti-aliasing support on mixed grids in PyFR for CPUs. Inparticular, cache blocking is used as an alternative to kernel fusion toeliminate unnecessary data movements between kernels at the main memory level.Specifically, kernels that exchange data are grouped together, and these groupsare then executed on small sub-regions of the domain that fit in per-coreprivate data cache. Additionally, cache blocking is also used to efficientlyimplement a tensor product factorisation of the interpolation operatorsassociated with anti-aliasing. By using cache blocking, the intermediateresults between application of the sparse factors are stored in per-coreprivate data cache, and a significant amount of data movement from main memoryis avoided. In order to assess the performance gains a theoretical model isdeveloped, and the implementation is benchmarked using a compressible 3DTaylor-Green vortex test case on both hexahedral and prismatic grids, withthird- and forth-order solution polynomials. The expected performance gainsbased on the theoretical model range from 1.99 to 2.62, and the speedupsobtained in practice range from 1.67 to 3.67 compared to PyFR v1.11.0.

Cache Blocking for Flux Reconstruction: Extension to Navier-Stokes Equations and Anti-aliasing