Abstract Near-optimal CPU efficiency for the basic operations of the Newton-Krylov-BILU iterations for the discontinuous Galerkin method (DGM) can be obtained by exploiting its data locality, as it allows to rewrite these operations in terms of BLAS/LAPACK operations on contiguous vectors and matrices of considerable size. Further significant enhancements are obtained by using single precision pre-conditioners and by exploiting data alignment to improve cache efficiency. The underlying data structures and operations are explained, followed by the comparison of the obtained floating point operation (FLOP) efficiency to the theoretical optimum.


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